A Monte-Carlo Sampling Framework For Reliable Evaluation of Large Language Models Using Behavioral Analysis (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Current approaches to evaluation of large language models ignore high entropy of LLM responses. |
| Approach: | They propose a Monte-Carlo evaluation framework for evaluating large language models . they test multiple LLMs to see if they are susceptible to cognitive biases . |
| Outcome: | The proposed framework shows that LLMs are more human-like and less rational . it also shows that larger LLM models are more susceptible to cognitive biases . |
Similar Papers
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
Copied to clipboard
Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
| Challenge: | Large Language Models (LLMs) have gained significant attention due to their capabilities in performing diverse tasks across domains. |
| Approach: | They review the primary challenges and limitations causing inconsistencies in evaluations . early models could generate coherent text but limited to simple tasks . |
| Outcome: | The proposed evaluations are reproducible, reliable, and robust. |
SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models (2025.naacl-industry)
Copied to clipboard
| Challenge: | Typical evaluations of Large Language Models (LLMs) report a single accuracy metric per dataset, often derived from an optimized setup. |
| Approach: | They propose a framework for non-adversarial evaluation of large language models that evaluates models by repeatedly testing them on the same benchmarks in various setups. |
| Outcome: | The proposed framework evaluates models by repeatedly testing them on the same benchmarks in various setups to give a realistic estimate of their accuracy and consistency. |
Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation (2025.coling-main)
Copied to clipboard
| Challenge: | LLMs are used to emulate sequential decision-making processes of humans . however, their ability to perform probabilistic sampling is limited . |
| Approach: | They propose to use large language models (LLMs) as agents to emulate the sequential decision-making processes of humans represented as Markov decision-makers (MDPs). |
| Outcome: | The proposed models can understand probabilities, but struggle with sampling precision . integrating coding tools can improve sampling precision, but this level of sampling precision still makes it difficult to simulate human behavior as agents. |
ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon the insights from disciplines such as user experience research and human behavioral psychology to ensure that the results are reliable. |
| Approach: | They propose a framework for human evaluation of generative large language models that takes into account usability, aesthetics and cognitive biases. |
| Outcome: | The proposed framework is based on the framework proposed by Deutsch and alnajjar . it is aimed at ensuring that human evaluation is accurate in the age of generative AI . |
A Survey of Confidence Estimation and Calibration in Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in various domains, but they can be unreliable due to factual errors in their generations. |
| Approach: | They summarize recent advances in LLM confidence estimation and calibration and outline their main lessons learned. |
| Outcome: | The proposed methods can be used to assess the reliability of models and to calibrate them across tasks. |
A Survey of Uncertainty Estimation Methods on Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities but could produce biased, hallucinated, or non-factual responses. |
| Approach: | They propose to conduct extensive experimental evaluations of LLM uncertainty estimation methods . large language models have demonstrated remarkable capabilities across tasks . |
| Outcome: | The proposed method could produce biased, hallucinated, or non-factual responses . a lack of comprehensive surveys on LLM uncertainty estimation is a problem . |
On Evaluating LLMs’ Capabilities as Functional Approximators: A Bayesian Evaluation Framework (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have revolutionized the way we can formulate tasks in text-in-text-out format. |
| Approach: | They propose a new evaluation framework to comprehensively assess LLMs’ function modeling abilities by adopting a Bayesian perspective of function modeling. |
| Outcome: | The proposed evaluation framework enables LLMs to excel in utilizing prior knowledge to develop a strong understanding of the underlying function. |
LLM Evaluate: An Industry-Focused Evaluation Tool for Large Language Models (2025.coling-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks in recent years. |
| Approach: | They propose to build an on-premise system for LLM evaluation to address the challenges in the evaluation of LLMs in real-world industrial settings. |
| Outcome: | The proposed evaluation system protects customer privacy and protects data integrity in real-world industrial environments. |
A Chinese Dataset for Evaluating the Safeguards in Large Language Models (2024.findings-acl)
Copied to clipboard
Yuxia Wang, Zenan Zhai, Haonan Li, Xudong Han, Shom Lin, Zhenxuan Zhang, Angela Zhao, Preslav Nakov, Timothy Baldwin
| Challenge: | a recent study has shown that large language models can produce harmful responses, exposing users to unexpected risks. |
| Approach: | They propose a dataset for the safety evaluation of Chinese LLMs in Mandarin Chinese . they extend the dataset to better identify false negative and false positive examples . |
| Outcome: | The proposed dataset is for the safety evaluation of Chinese LLMs, and is based on a Chinese dataset. |
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? (2024.findings-eacl)
Copied to clipboard
Rishav Hada, Varun Gumma, Adrian Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Language Models (LLMs) excel in various tasks, but their evaluation, especially in languages beyond the top 20, remains inadequate due to existing benchmarks and metrics limitations. |
| Approach: | They propose to use Large Language Models as evaluators to rank or score other models’ outputs by calibrating them against 20K human judgments across three text-generation tasks, five metrics, and eight languages. |
| Outcome: | The proposed evaluation methods can be used to improve multilingual evaluation by calibrating them against 20K human judgments across three text-generation tasks, five metrics, and eight languages. |