Predicting Question-Answering Performance of Large Language Models through Semantic Consistency

• URL: http://arxiv.org/abs/2311.01152v1
• Date: Thu, 2 Nov 2023 11:27:21 GMT
• Title: Predicting Question-Answering Performance of Large Language Models through Semantic Consistency
• Authors: Ella Rabinovich, Samuel Ackerman, Orna Raz, Eitan Farchi, Ateret Anaby-Tavor
• Abstract summary: We address the task of assessing question-answering semantic consistency of large language models. We create a benchmark dataset with high-quality paraphrases for factual questions, and release the dataset to the community. We build and evaluate a framework for factual QA reference-less performance prediction.
• Score: 5.857193811761703
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic consistency of a language model is broadly defined as the model's ability to produce semantically-equivalent outputs, given semantically-equivalent inputs. We address the task of assessing question-answering (QA) semantic consistency of contemporary large language models (LLMs) by manually creating a benchmark dataset with high-quality paraphrases for factual questions, and release the dataset to the community. We further combine the semantic consistency metric with additional measurements suggested in prior work as correlating with LLM QA accuracy, for building and evaluating a framework for factual QA reference-less performance prediction -- predicting the likelihood of a language model to accurately answer a question. Evaluating the framework on five contemporary LLMs, we demonstrate encouraging, significantly outperforming baselines, results.

Related papers

• PPLqa: An Unsupervised Information-Theoretic Quality Metric for Comparing Generative Large Language Models [9.027802326177135]
  We propose PPLqa, an easy to compute, language independent, information-theoretic metric to measure the quality of responses of generative Large Language Models (LLMs) The method and metric enables users to rank generative language models for quality of responses, so as to make a selection of the best model for a given task.
  arXiv  Detail & Related papers  (2024-11-22T19:28:06Z)
• Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback [50.84142264245052]
  This work introduces the Align-SLM framework to enhance the semantic understanding of textless Spoken Language Models (SLMs) Our approach generates multiple speech continuations from a given prompt and uses semantic metrics to create preference data for Direct Preference Optimization (DPO) We evaluate the framework using ZeroSpeech 2021 benchmarks for lexical and syntactic modeling, the spoken version of the StoryCloze dataset for semantic coherence, and other speech generation metrics, including the GPT4-o score and human evaluation.
  arXiv  Detail & Related papers  (2024-11-04T06:07:53Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• A Large-Scale Evaluation of Speech Foundation Models [110.95827399522204]
  We establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the foundation model paradigm for speech. We propose a unified multi-tasking framework to address speech processing tasks in SUPERB using a frozen foundation model followed by task-specialized, lightweight prediction heads.
  arXiv  Detail & Related papers  (2024-04-15T00:03:16Z)
• 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)
• Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy [27.454549324141087]
  We propose a novel VQA benchmark based on well-known visual classification datasets. We also suggest using the semantic hierarchy of the label space to ask automatically generated follow-up questions about the ground-truth category. Our contributions aim to lay the foundation for more precise and meaningful assessments.
  arXiv  Detail & Related papers  (2024-02-11T18:26:18Z)
• Semantic Consistency for Assuring Reliability of Large Language Models [9.876355290198639]
  Large Language Models (LLMs) exhibit remarkable fluency and competence across various natural language tasks. We introduce a general measure of semantic consistency, and formulate multiple versions of this metric to evaluate the performance of various LLMs. We propose a novel prompting strategy, called Ask-to-Choose (A2C), to enhance semantic consistency.
  arXiv  Detail & Related papers  (2023-08-17T18:11:33Z)
• Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge [59.22170796793179]
  Transformers Language Models (TLMs) were tested on a benchmark for the textitdynamic estimation of thematic fit Our results show that TLMs can reach performances that are comparable to those achieved by SDM. However, additional analysis consistently suggests that TLMs do not capture important aspects of event knowledge.
  arXiv  Detail & Related papers  (2021-07-22T20:52:26Z)
• Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little [74.49773960145681]
  A possible explanation for the impressive performance of masked language model (MLM)-training is that such models have learned to represent the syntactic structures prevalent in NLP pipelines. In this paper, we propose a different explanation: pre-trains succeed on downstream tasks almost entirely due to their ability to model higher-order word co-occurrence statistics. Our results show that purely distributional information largely explains the success of pre-training, and underscore the importance of curating challenging evaluation datasets that require deeper linguistic knowledge.
  arXiv  Detail & Related papers  (2021-04-14T06:30:36Z)

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.