Language Models can Evaluate Themselves via Probability Discrepancy (2024)

Abstract: In this paper, we begin by illustrating that, when presented with a query, Large Language Models (LLMs) capable of providing accurate responses tend to exhibit a more uniform probability distribution compared to their less proficient counterparts. Building upon this observation, we introduce a novel self-assessment criterion termed ProbDiff for evaluating the performance of diverse LLMs. This method eliminates the need for training an additional evaluation model or relying on external proprietary models such as GPT-4 as a judger. Instead, it solely relies on the LLMs under evaluation to compute the probability discrepancy between the original response generation and its revised versions. A higher discrepancy in two LLMs for the same query suggests a relatively weaker ability. We discover that ProbDiff yields comparable results to mainstream GPT-4-based evaluations on various scenarios including NLG tasks like translation and summarization, as well as LLM evaluation benchmarks such as AlignBench, MT-Bench, and AlpacaEval, across LLMs of different sizes.

## introduction with the emergence of llms like chatgpt, we've witnessed groundbreaking progress in tasks involving instruction following @xcite , intent comprehension @xcite , and text generation @xcite . as llms evolve at a rapid pace, it becomes crucial to develop solid evaluation frameworks to gauge their performance accurately @xcite . traditional evaluation methods such as bleu (papineni et al., 2002) † work done during the author's internship at the alibaba group * corresponding authors focus mainly on superficial text differences and often fail to align with human judgment @xcite . a is the father of b. b is the father of c. what is the relationship between a and c? a is the grandfather of c. a is the grandfather of c. ## method in this section, we present our self-evaluation protocol, probdiff, designed to self-assess the capabilities of llms autonomously through logarithmic probability changes. we begin with prelimi-nary studies to validate the prior assumptions of probdiff, followed by a detailed exposition of its intricacies and mechanisms. ## experiment in this section, we evaluate our probdiff methodology across three diverse natural language generation tasks: translation, summarization, and a niche task designated as xiaohongshu blog writing. to gauge probdiff's efficacy, we fine-tuned the qwen-14b-chat model for each specified task. additionally, our evaluation encompasses three benchmarks tailored for llm assessment: mt-bench @xcite , alpacaeval @xcite , and alignbench @xcite . these experiments are designed to comprehensively evaluate probdiff's performance across a wide array of linguistic tasks and benchmarks, showcasing its applicability in measuring llm effectiveness. ## conclusion in this paper, we introduce an innovative evaluation framework, probdiff, designed to facilitate llms in performing self-assessment through the analysis of probability discrepancies in their generated outputs. furthermore, our probdiff method offers a robust mechanism for evaluating the competencies of llms, even without preexisting training or domain-specific benchmarks, effectively addressing concerns of data confidentiality breaches.