The Generative AI Paradox on Evaluation: What It Can Solve, It May Not
Evaluate
- URL: http://arxiv.org/abs/2402.06204v1
- Date: Fri, 9 Feb 2024 06:16:08 GMT
- Title: The Generative AI Paradox on Evaluation: What It Can Solve, It May Not
Evaluate
- Authors: Juhyun Oh, Eunsu Kim, Inha Cha, Alice Oh
- Abstract summary: This paper explores the assumption that Large Language Models (LLMs) skilled in generation tasks are equally adept as evaluators.
We assess the performance of three LLMs and one open-source LM in Question-Answering (QA) and evaluation tasks using the TriviaQA dataset.
- Score: 17.77014177096838
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper explores the assumption that Large Language Models (LLMs) skilled
in generation tasks are equally adept as evaluators. We assess the performance
of three LLMs and one open-source LM in Question-Answering (QA) and evaluation
tasks using the TriviaQA (Joshi et al., 2017) dataset. Results indicate a
significant disparity, with LLMs exhibiting lower performance in evaluation
tasks compared to generation tasks. Intriguingly, we discover instances of
unfaithful evaluation where models accurately evaluate answers in areas where
they lack competence, underscoring the need to examine the faithfulness and
trustworthiness of LLMs as evaluators. This study contributes to the
understanding of "the Generative AI Paradox" (West et al., 2023), highlighting
a need to explore the correlation between generative excellence and evaluation
proficiency, and the necessity to scrutinize the faithfulness aspect in model
evaluations.
Related papers
- Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles [20.18736445118689]
We introduce SPLAT, a benchmark leveraging Situation Puzzles to evaluate and elicit lateral thinking of Large Language Models (LLMs)
This benchmark, containing 975 graded situation puzzles across three difficulty levels, employs a new multi-turn player-judge framework instead of the traditional model-based evaluation.
Experiments demonstrate that a robust evaluation model, such as WizardLM-2, closely matches human judgements in both intermediate question-answering and final scenario accuracy.
arXiv Detail & Related papers (2024-10-09T10:09:11Z) - From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management [6.70908766695241]
This study explores the potential of Large Language Models (LLMs), specifically GPT-4, to enhance objectivity in organizational task performance evaluations.
Our results suggest that GPT ratings are comparable to human ratings but exhibit higher consistency and reliability.
Our research suggests that while LLMs are capable of extracting meaningful constructs from text-based data, their scope is currently limited to specific forms of performance evaluation.
arXiv Detail & Related papers (2024-08-09T20:35:10Z) - LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks [106.09361690937618]
There is an increasing trend towards evaluating NLP models with LLMs instead of human judgments.
We provide JUDGE-BENCH, a collection of 20 NLP datasets with human annotations covering a broad range of evaluated properties and types of data.
We evaluate 11 current LLMs, covering both open-weight and proprietary models, for their ability to replicate the annotations.
arXiv Detail & Related papers (2024-06-26T14:56:13Z) - Finding Blind Spots in Evaluator LLMs with Interpretable Checklists [23.381287828102995]
We investigate the effectiveness of Large Language Models (LLMs) as evaluators for text generation tasks.
We propose FBI, a novel framework designed to examine the proficiency of Evaluator LLMs in assessing four critical abilities.
arXiv Detail & Related papers (2024-06-19T10:59:48Z) - DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
Large Language Models (LLMs) are scalable and economical evaluators.
The question of how reliable these evaluators are has emerged as a crucial research question.
We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
arXiv Detail & Related papers (2024-05-24T08:12:30Z) - LOVA3: Learning to Visual Question Answering, Asking and Assessment [61.51687164769517]
Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge.
Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills.
We introduce LOVA3, an innovative framework named "Learning tO Visual question Answering, Asking and Assessment"
arXiv Detail & Related papers (2024-05-23T18:21:59Z) - Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
We propose a new evaluation method, SQC-Score.
Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score.
Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
arXiv Detail & Related papers (2024-04-04T15:36:53Z) - LLMs as Narcissistic Evaluators: When Ego Inflates Evaluation Scores [23.568883428947494]
We investigate whether prominent LM-based evaluation metrics demonstrate a favorable bias toward their respective underlying LMs in the context of summarization tasks.
Our findings unveil a latent bias, particularly pronounced when such evaluation metrics are used in a reference-free manner without leveraging gold summaries.
These results underscore that assessments provided by generative evaluation models can be influenced by factors beyond the inherent text quality.
arXiv Detail & Related papers (2023-11-16T10:43:26Z) - Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks [65.69651759036535]
We analyze whether large language models (LLMs) can serve as reliable alternatives to humans.
This paper explores both conventional tasks (e.g., story generation) and alignment tasks (e.g., math reasoning)
We find that LLM evaluators can generate unnecessary criteria or omit crucial criteria, resulting in a slight deviation from the experts.
arXiv Detail & Related papers (2023-10-30T17:04:35Z) - A Survey on Evaluation of Large Language Models [87.60417393701331]
Large language models (LLMs) are gaining increasing popularity in both academia and industry.
This paper focuses on three key dimensions: what to evaluate, where to evaluate, and how to evaluate.
arXiv Detail & Related papers (2023-07-06T16:28:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.