Revisiting Challenges and Hazards in Large Language Model Evaluation

  1. Lopez-Gazpio, Inigo
Revista:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Año de publicación: 2024

Título del ejemplar: Procesamiento del Lenguaje Natural, Revista nº 72, marzo de 2024

Número: 72

Páginas: 15-30

Tipo: Artículo

Otras publicaciones en: Procesamiento del lenguaje natural

Resumen

En la era de los modelos de lenguaje de gran escala, el objetivo de la inteligencia artificial ha evolucionado para asistir a personas de maneras sin precedentes conocidos. A medida que los modelos se integran en la sociedad, aumenta la necesidad de evaluaciones exhaustivas. La aceptación de estos sistemas en el mundo real depende de sus habilidades de conocimiento, razonamiento y argumentación. Sin embargo, estándares inconsistentes entre dominios complican la evaluación, dificultando la comparación de modelos y la comprensión de su funcionamiento. Nuestro estudio se enfoca en organizar y aclarar los procesos de evaluación de estos modelos. Examinamos investigaciones recientes para analizar las tendencias actuales e investigar si los métodos de evaluación se ajustan a los requisitos del progreso. Finalmente, identificamos y detallamos los principales desafíos y riesgos que afectan la evaluación, un área que aún no ha sido explorada extensamente. Este enfoque es necesario para reconocer las limitaciones actuales, el potencial y las particularidades de la evaluación de estos sistemas.

Referencias bibliográficas

  • Aftan, S. and H. Shah. 2023. A survey on bert and its applications. In 2023 20th Learning and Technology Conference (L&T), pages 161–166. IEEE.
  • Aiyappa, R., J. An, H. Kwak, and Y.-Y. Ahn. 2023. Can we trust the evaluation on chatgpt? arXiv preprint arXiv:2303.12767.
  • Awasthi, I., K. Gupta, P. S. Bhogal, S. S. Anand, and P. K. Soni. 2021. Natural language processing (nlp) based text summarization-a survey. In 2021 6th International Conference on Inventive Computation Technologies (ICICT), pages 1310–1317. IEEE.
  • Baltrusaitis, T., C. Ahuja, and L.-P. Morency. 2018. Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence, 41(2):423–443.
  • Bang, Y., S. Cahyawijaya, N. Lee, W. Dai, D. Su, B. Wilie, H. Lovenia, Z. Ji, T. Yu, W. Chung, et al. 2023. A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023.
  • Baradaran, R., R. Ghiasi, and H. Amirkhani. 2022. A survey on machine reading comprehension systems. Natural Language Engineering, 28(6):683–732.
  • Baroiu, A.-C. and S. Trausan-Matu. 2023. How capable are state-of-the-art language models to cope with sarcasm? In 2023 24th International Conference on Control Systems and Computer Science (CSCS), pages 399–402. IEEE.
  • Bates, M. 1995. Models of natural language understanding. Proceedings of the National Academy of Sciences, 92(22):9977–9982.
  • Berglund, L., M. Tong, M. Kaufmann, M. Balesni, A. C. Stickland, T. Korbak, and O. Evans. 2023. The reversal curse: Llms trained on.a is b”fail to learn”b is a”. arXiv preprint arXiv:2309.12288.
  • Bommasani, R., D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
  • Bouziane, A., D. Bouchiha, N. Doumi, and M. Malki. 2015. Question answering systems: survey and trends. Procedia Computer Science, 73:366–375.
  • Bradbury, J., R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, et al. 2018. Jax: Composable transformations of python+numpy programs (v0. 2.5). Software available from https://github. com/google/jax.
  • Brown, T., B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
  • Bubeck, S., V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.
  • Buchanan, B. G. and E. H. Shortliffe. 1984. Rule based expert systems: the mycin experiments of the stanford heuristic programming project (the Addison-Wesley series in artificial intelligence). Addison-Wesley Longman Publishing Co., Inc.
  • Chang, Y., X. Wang, J. Wang, Y. Wu, K. Zhu, H. Chen, L. Yang, X. Yi, C. Wang, Y. Wang, et al. 2023. A survey on evaluation of large language models. arXiv preprint arXiv:2307.03109.
  • Chen, M., J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.
  • Chen, Y., A. Arunasalam, and Z. B. Celik. 2023. Can large language models provide security & privacy advice? measuring the ability of llms to refute misconceptions. arXiv preprint arXiv:2310.02431.
  • Chetnani, Y. P. 2023. Evaluating the Impact of Model Size on Toxicity and Stereotyping in Generative LLM. Ph.D. thesis, State University of New York at Buffalo.
  • Chiang, C.-H. and H.-y. Lee. 2023. Can large language models be an alternative to human evaluations? arXiv preprint arXiv:2305.01937.
  • Chollet, F. 2019. On the measure of intelligence. arXiv preprint arXiv:1911.01547.
  • Chowdhery, A., S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al. 2022. Palm: Scaling language modelling with pathways. arXiv preprint arXiv:2204.02311.
  • Clark, E., S. Rijhwani, S. Gehrmann, J. Maynez, R. Aharoni, V. Nikolaev, T. Sellam, A. Siddhant, D. Das, and A. P. Parikh. 2023. Seahorse: A multilingual, multifaceted dataset for summarization evaluation. arXiv preprint arXiv:2305.13194.
  • Cobbe, K., V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, et al. 2021. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168.
  • Costa-jussà, M. R., J. Cross, O. Çelebi, M. Elbayad, K. Heafield, K. Heffernan, E. Kalbassi, J. Lam, D. Licht, J. Maillard, et al. 2022. No language left behind: Scaling human-centered machine translation. arXiv preprint arXiv:2207.04672.
  • Costantini, S. 2002. Meta-reasoning: A survey. In Computational Logic: Logic Programming and Beyond: Essays in Honour of Robert A. Kowalski Part II. Springer, pages 253–288.
  • Creswell, A., M. Shanahan, and I. Higgins. 2022. Selection-inference: Exploiting large language models for interpretable logical reasoning. arXiv preprint arXiv:2205.09712.
  • de Wynter, A., X. Wang, A. Sokolov, Q. Gu, and S.-Q. Chen. 2023. An evaluation on large language model outputs: Discourse and memorization. arXiv preprint arXiv:2304.08637.
  • Demarco, F., J. M. O. de Zarate, and E. Feuerstein. 2023. Measuring ideological spectrum through nlp. In Proceedings of the Seventh Workshop on Natural Language for Artificial Intelligence (NL4AI 2023) co-located with 22th International Conference of the Italian Association for Artificial Intelligence (AI* IA 2023).
  • Demetriadis, S. and Y. Dimitriadis. 2023. Conversational agents and language models that learn from human dialogues to support design thinking. In International Conference on Intelligent Tutoring Systems, pages 691–700. Springer.
  • Deng, J. and Y. Lin. 2022. The benefits and challenges of chatgpt: An overview. Frontiers in Computing and Intelligent Systems, 2(2):81–83.
  • Devlin, J., M.-W. Chang, K. Lee, and K. Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  • Dong, G., J. Zhao, T. Hui, D. Guo, W. Wang, B. Feng, Y. Qiu, Z. Gongque, K. He, Z. Wang, et al. 2023. Revisit input perturbation problems for llms: A unified robustness evaluation framework for noisy slot filling task. In CCF International Conference on Natural Language Processing and Chinese Computing, pages 682–694. Springer.
  • Erdem, E., M. Kuyu, S. Yagcioglu, A. Frank, L. Parcalabescu, B. Plank, A. Babii, O. Turuta, A. Erdem, I. Calixto, et al. 2022. Neural natural language generation: A survey on multilinguality, multimodality, controllability and learning. Journal of Artificial Intelligence Research, 73:1131–1207.
  • Floridi, L. and M. Chiriatti. 2020. Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines, 30:681–694.
  • Frieder, S., L. Pinchetti, R.-R. Griffiths, T. Salvatori, T. Lukasiewicz, P. C. Petersen, A. Chevalier, and J. Berner. 2023. Mathematical capabilities of chatgpt. arXiv preprint arXiv:2301.13867.
  • Fu, Y., H. Peng, and T. Khot. 2022. How does gpt obtain its ability? tracing emergent abilities of language models to their sources. Yao Fu’s Notion.
  • Gamallo, P., J. R. P. Campos, and I. Alegria. 2017. A perplexity-based method for similar languages discrimination. In Proceedings of the fourth workshop on NLP for similar languages, varieties and dialects (VarDial), pages 109–114.
  • Gao, J. and C.-Y. Lin. 2004. Introduction to the special issue on statistical language modeling. ACM Transactions on Asian Language Information Processing (TALIP), 3(2):87–93.
  • Garg, T., S. Masud, T. Suresh, and T. Chakraborty. 2023. Handling bias in toxic speech detection: A survey. ACM Computing Surveys, 55(13s):1–32.
  • Ghazal, A., T. Rabl, M. Hu, F. Raab, M. Poess, A. Crolotte, and H.-A. Jacobsen. 2013. Bigbench: Towards an industry standard benchmark for big data analytics. In Proceedings of the 2013 ACM SIGMOD international conference on Management of data, pages 1197–1208.
  • Hadi, M. U., R. Qureshi, A. Shah, M. Irfan, A. Zafar, M. Shaikh, N. Akhtar, J.Wu, and S. Mirjalili. 2023a. A survey on large language models: Applications, challenges, limitations, and practical usage. TechRxiv.
  • Hadi, M. U., R. Qureshi, A. Shah, M. Irfan, A. Zafar, M. B. Shaikh, N. Akhtar, J. Wu, S. Mirjalili, et al. 2023b. Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects. Authorea Preprints.
  • Head, C. B., P. Jasper, M. McConnachie, L. Raftree, and G. Higdon. 2023. Large language model applications for evaluation: Opportunities and ethical implications. New Directions for Evaluation, 2023(178-179):33–46.
  • Hendrycks, D., C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt. 2020. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300.
  • Hoffmann, J., S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, D. d. L. Casas, L. A. Hendricks, J. Welbl, A. Clark, et al. 2022. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556.
  • Huang, D., Q. Bu, J. Zhang, X. Xie, J. Chen, and H. Cui. 2023. Bias assessment and mitigation in llm-based code generation. arXiv preprint arXiv:2309.14345.
  • Jain, N., K. Saifullah, Y. Wen, J. Kirchenbauer, M. Shu, A. Saha, M. Goldblum, J. Geiping, and T. Goldstein. 2023. Bring your own data! self-supervised evaluation for large language models. arXiv preprint arXiv:2306.13651.
  • Ji, Z., N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung. 2023. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12):1–38.
  • Jin, Z., J. Liu, Z. Lyu, S. Poff, M. Sachan, R. Mihalcea, M. Diab, and B. Schölkopf. 2023. Can large language models infer causation from correlation? arXiv preprint arXiv:2306.05836.
  • Kaplan, J., S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei. 2020. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.
  • Kasneci, E., K. Seßler, S. K¨uchemann, M. Bannert, D. Dementieva, F. Fischer, U. Gasser, G. Groh, S. G¨unnemann, E. Hüllermeier, et al. 2023. Chatgpt for good? on opportunities and challenges of large language models for education. Learning and individual differences, 103:102274.
  • Kejriwal, M., H. Santos, K. Shen, A. M. Mulvehill, and D. L. McGuinness. 2023. Context-rich evaluation of machine common sense. In International Conference on Artificial General Intelligence, pages 167–176. Springer.
  • Khalfa, J. 1994. What is intelligence? Cambridge University Press.
  • Khowaja, S. A., P. Khuwaja, and K. Dev. 2023. Chatgpt needs spade (sustainability, privacy, digital divide, and ethics) evaluation: A review. arXiv preprint arXiv:2305.03123.
  • Koh, J. Y., R. Salakhutdinov, and D. Fried. 2023. Grounding language models to images for multimodal generation. arXiv preprint arXiv:2301.13823.
  • Korb, K. B. and A. E. Nicholson. 2010. Bayesian artificial intelligence. CRC press.
  • Kotek, H., R. Dockum, and D. Sun. 2023. Gender bias and stereotypes in large language models. In Proceedings of The ACM Collective Intelligence Conference, pages 12–24.
  • Lacave, C. and F. J. Díez. 2002. A review of explanation methods for bayesian networks. The Knowledge Engineering Review, 17(2):107–127.
  • Lai, V. D., N. T. Ngo, A. P. B. Veyseh, H. Man, F. Dernoncourt, T. Bui, and T. H. Nguyen. 2023. Chatgpt beyond english: Towards a comprehensive evaluation of large language models in multilingual learning. arXiv preprint arXiv:2304.05613.
  • Lazarski, E., M. Al-Khassaweneh, and C. Howard. 2021. Using nlp for fact checking: A survey. Designs, 5(3):42.
  • Lehman, J., J. Gordon, S. Jain, K. Ndousse, C. Yeh, and K. O. Stanley. 2023. Evolution through large models. In Handbook of Evolutionary Machine Learning. Springer, pages 331–366.
  • Li, J., T. Tang, W. X. Zhao, J.-Y. Nie, and J.-R. Wen. 2022. Pretrained language models for text generation: A survey. arXiv preprint arXiv:2201.05273.
  • Li, Y., M. Du, R. Song, X. Wang, and Y. Wang. 2023. A survey on fairness in large language models. arXiv preprint arXiv:2308.10149.
  • Liang, P., R. Bommasani, T. Lee, D. Tsipras, D. Soylu, M. Yasunaga, Y. Zhang, D. Narayanan, Y. Wu, A. Kumar, et al. 2022. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110.
  • Lin, S., J. Hilton, and O. Evans. 2021. Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958.
  • Liu, F., E. Bugliarello, E. M. Ponti, S. Reddy, N. Collier, and D. Elliott. 2021. Visually grounded reasoning across languages and cultures. arXiv preprint arXiv:2109.13238.
  • Ma, J.-Y., J.-C. Gu, Z.-H. Ling, Q. Liu, and C. Liu. 2023. Untying the reversal curse via bidirectional language model editing. arXiv preprint arXiv:2310.10322.
  • Mahany, A., H. Khaled, N. S. Elmitwally, N. Aljohani, and S. Ghoniemy. 2022. Negation and speculation in nlp: A survey, corpora, methods, and applications. Applied Sciences, 12(10):5209.
  • McDonald, D. D. 2010. Natural language generation. Handbook of natural language processing, 2:121–144.
  • Min, B., H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth. 2023. Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys, 56(2):1–40.
  • Motger, Q., X. Franch, and J. Marco. 2022. Software-based dialogue systems: survey, taxonomy, and challenges. ACM Computing Surveys, 55(5):1–42.
  • Nijkamp, E., H. Hayashi, C. Xiong, S. Savarese, and Y. Zhou. 2023. Codegen2: Lessons for training llms on programming and natural languages. arXiv preprint arXiv:2305.02309.
  • Novikova, J., O. Duˇsek, A. C. Curry, and V. Rieser. 2017. Why we need new evaluation metrics for nlg. arXiv preprint arXiv:1707.06875.
  • OpenAI, R. 2023. Gpt-4 technical report. Arxiv 2303.08774. View in Article, 2.
  • Orrù, G., A. Piarulli, C. Conversano, and A. Gemignani. 2023. Human-like problem-solving abilities in large language models using chatgpt. Frontiers in Artificial Intelligence, 6:1199350.
  • Oshikawa, R., J. Qian, and W. Y.Wang. 2018. A survey on natural language processing for fake news detection. arXiv preprint arXiv:1811.00770.
  • Ouyang, L., J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744.
  • Peng, Z., Z. Wang, and D. Deng. 2023. Nearduplicate sequence search at scale for large language model memorization evaluation. Proceedings of the ACM on Management of Data, 1(2):1–18.
  • Perez, E., S. Huang, F. Song, T. Cai, R. Ring, J. Aslanides, A. Glaese, N. McAleese, and G. Irving. 2022. Red teaming language models with language models. arXiv preprint arXiv:2202.03286.
  • Puchert, P., P. Poonam, C. van Onzenoodt, and T. Ropinski. 2023. Llmmaps–a visual metaphor for stratified evaluation of large language models. arXiv preprint arXiv:2304.00457.
  • Qin, C., A. Zhang, Z. Zhang, J. Chen, M. Yasunaga, and D. Yang. 2023. Is chatgpt a general-purpose natural language processing task solver? arXiv preprint arXiv:2302.06476.
  • Reiter, E. 2018. A structured review of the validity of bleu. Computational Linguistics, 44(3):393–401.
  • Rillig, M. C., M. ˚Agerstrand, M. Bi, K. A. Gould, and U. Sauerland. 2023. Risks and benefits of large language models for the environment. Environmental Science & Technology, 57(9):3464–3466.
  • Ruder, S., J. H. Clark, A. Gutkin, M. Kale, M. Ma, M. Nicosia, S. Rijhwani, P. Riley, J.-M. A. Sarr, X. Wang, et al. 2023. Xtreme-up: A user-centric scarcedata benchmark for under-represented languages. arXiv preprint arXiv:2305.11938.
  • Saha, T., D. Ganguly, S. Saha, and P. Mitra. 2023. Workshop on large language models’ interpretability and trustworthiness (llmit). In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pages 5290–5293.
  • Sainz, O., J. A. Campos, I. García-Ferrero, J. Etxaniz, O. L. de Lacalle, and E. Agirre. 2023. Nlp evaluation in trouble: On the need to measure llm data contamination for each benchmark. arXiv preprint arXiv:2310.18018.
  • Sakaguchi, K., R. L. Bras, C. Bhagavatula, and Y. Choi. 2021. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99–106.
  • Salloum, S. A., R. Khan, and K. Shaalan. 2020. A survey of semantic analysis approaches. In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), pages 61–70. Springer.
  • Shanahan, M. 2022. Talking about large language models. arXiv preprint arXiv:2212.03551.
  • Shin, J. and J. Nam. 2021. A survey of automatic code generation from natural language. Journal of Information Processing Systems, 17(3):537–555.
  • Song, G., Y. Ye, X. Du, X. Huang, and S. Bie. 2014. Short text classification: a survey. Journal of multimedia, 9(5).
  • Srivastava, A., A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso, et al. 2022. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615.
  • Storks, S., Q. Gao, and J. Y. Chai. 2019. Recent advances in natural language inference: A survey of benchmarks, resources, and approaches. arXiv preprint arXiv:1904.01172.
  • Sun, J., S. Wang, J. Zhang, and C. Zong. 2020. Distill and replay for continual language learning. In Proceedings of the 28th international conference on computational linguistics, pages 3569–3579.
  • Tang, R., Y.-N. Chuang, and X. Hu. 2023. The science of detecting llm-generated texts. arXiv preprint arXiv:2303.07205.
  • Tedeschi, S., J. Bos, T. Declerck, J. Hajic, D. Hershcovich, E. H. Hovy, A. Koller, S. Krek, S. Schockaert, R. Sennrich, et al. 2023. What’s the meaning of superhuman performance in today’s nlu? arXiv preprint arXiv:2305.08414.
  • Touvron, H., T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
  • Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, 30.
  • Wang, A., Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman. 2019. Superglue: A stickier benchmark for general-purpose language understanding systems. Advances in neural information processing systems, 32.
  • Wang, A., A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. 2018. Glue: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461.
  • Wang, X., J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowdhery, and D. Zhou. 2022. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171.
  • Wang, Y., Y. Wang, J. Liu, and Z. Liu. 2020. A comprehensive survey of grammar error correction. arXiv preprint arXiv:2005.06600.
  • Wei, J., Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, et al. 2022. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682.
  • Xu, F. F., U. Alon, G. Neubig, and V. J. Hellendoorn. 2022. A systematic evaluation of large language models of code. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, pages 1–10.
  • Xu, P., W. Shao, K. Zhang, P. Gao, S. Liu, M. Lei, F. Meng, S. Huang, Y. Qiao, and P. Luo. 2023a. Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models. arXiv preprint arXiv:2306.09265.
  • Xu, X., K. Kong, N. Liu, L. Cui, D. Wang, J. Zhang, and M. Kankanhalli. 2023b. An llm can fool itself: A prompt-based adversarial attack. arXiv preprint arXiv:2310.13345.
  • Zellers, R., A. Holtzman, Y. Bisk, A. Farhadi, and Y. Choi. 2019. Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830.
  • Zhai, Y., S. Tong, X. Li, M. Cai, Q. Qu, Y. J. Lee, and Y. Ma. 2023. Investigating the catastrophic forgetting in multimodal large language models. arXiv preprint arXiv:2309.10313.
  • Zhang, L., S. Wang, and B. Liu. 2018. Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4):e1253.
  • Zhang, R., J. Han, A. Zhou, X. Hu, S. Yan, P. Lu, H. Li, P. Gao, and Y. Qiao. 2023. Llama-adapter: Efficient fine-tuning of language models with zero-init attention. arXiv preprint arXiv:2303.16199.
  • Zhao, W. X., K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al. 2023. A survey of large language models. arXiv e-prints, pages arXiv–2303.
  • Zhong, W., R. Cui, Y. Guo, Y. Liang, S. Lu, Y.Wang, A. Saied, W. Chen, and N. Duan. 2023. Agieval: A human-centric benchmark for evaluating foundation models. arXiv preprint arXiv:2304.06364.
  • Zhu, K., J. Wang, J. Zhou, Z. Wang, H. Chen, Y. Wang, L. Yang, W. Ye, N. Z. Gong, Y. Zhang, et al. 2023. Promptbench: Towards evaluating the robustness of large language models on adversarial prompts. arXiv preprint arXiv:2306.04528.