VERITAS: A Unified Approach to Reliability Evaluation

• URL: http://arxiv.org/abs/2411.03300v1
• Date: Tue, 05 Nov 2024 17:53:25 GMT
• Title: VERITAS: A Unified Approach to Reliability Evaluation
• Authors: Rajkumar Ramamurthy, Meghana Arakkal Rajeev, Oliver Molenschot, James Zou, Nazneen Rajani,
• Abstract summary: Large language models (LLMs) often fail to synthesize information from their context to generate an accurate response. VERITAS is a family of hallucination detection models designed to operate flexibly across diverse contexts.
• Score: 26.051109586419308
• License:
• Abstract: Large language models (LLMs) often fail to synthesize information from their context to generate an accurate response. This renders them unreliable in knowledge intensive settings where reliability of the output is key. A critical component for reliable LLMs is the integration of a robust fact-checking system that can detect hallucinations across various formats. While several open-access fact-checking models are available, their functionality is often limited to specific tasks, such as grounded question-answering or entailment verification, and they perform less effectively in conversational settings. On the other hand, closed-access models like GPT-4 and Claude offer greater flexibility across different contexts, including grounded dialogue verification, but are hindered by high costs and latency. In this work, we introduce VERITAS, a family of hallucination detection models designed to operate flexibly across diverse contexts while minimizing latency and costs. VERITAS achieves state-of-the-art results considering average performance on all major hallucination detection benchmarks, with $10\%$ increase in average performance when compared to similar-sized models and get close to the performance of GPT4 turbo with LLM-as-a-judge setting.

Related papers

• A Debate-Driven Experiment on LLM Hallucinations and Accuracy [7.821303946741665]
  This study investigates the phenomenon of hallucination in large language models (LLMs) Multiple instances of GPT-4o-Mini models engage in a debate-like interaction prompted with questions from the TruthfulQA dataset. One model is deliberately instructed to generate plausible but false answers while the other models are asked to respond truthfully.
  arXiv  Detail & Related papers  (2024-10-25T11:41:27Z)
• FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows" [74.7488607599921]
  FaithEval is a benchmark to evaluate the faithfulness of large language models (LLMs) in contextual scenarios. FaithEval comprises 4.9K high-quality problems in total, validated through a rigorous four-stage context construction and validation framework.
  arXiv  Detail & Related papers  (2024-09-30T06:27:53Z)
• THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models [0.0]
  Hallucination, the generation of factually incorrect content, is a growing challenge in Large Language Models. This paper introduces THaMES, an integrated framework and library addressing this gap. THaMES offers an end-to-end solution for evaluating and mitigating hallucinations in LLMs.
  arXiv  Detail & Related papers  (2024-09-17T16:55:25Z)
• On the Worst Prompt Performance of Large Language Models [93.13542053835542]
  Performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts. We introduce RobustAlpacaEval, a new benchmark that consists of semantically equivalent case-level queries. Experiments on RobustAlpacaEval with ChatGPT and six open-source LLMs from the Llama, Mistral, and Gemma families uncover substantial variability in model performance.
  arXiv  Detail & Related papers  (2024-06-08T13:40:38Z)
• MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models [70.92847554971065]
  We introduce MT-Eval, a comprehensive benchmark designed to evaluate multi-turn conversational abilities. By analyzing human-LLM conversations, we categorize interaction patterns into four types: recollection, expansion, refinement, and follow-up. Our evaluation of 11 well-known LLMs shows that while closed-source models generally surpass open-source ones, certain open-source models exceed GPT-3.5-Turbo in specific tasks.
  arXiv  Detail & Related papers  (2024-01-30T04:50:28Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• FactCHD: Benchmarking Fact-Conflicting Hallucination Detection [64.4610684475899]
  FactCHD is a benchmark designed for the detection of fact-conflicting hallucinations from LLMs. FactCHD features a diverse dataset that spans various factuality patterns, including vanilla, multi-hop, comparison, and set operation. We introduce Truth-Triangulator that synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2.
  arXiv  Detail & Related papers  (2023-10-18T16:27:49Z)
• Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task [18.623619585980688]
  We propose a unified robustness evaluation framework based on the slot-filling task to evaluate the dialogue understanding capability of large language models. Specifically, we construct a input perturbation evaluation dataset, Noise-LLM, which contains five types of single perturbation and four types of mixed perturbation data. Our aim is to assess how well various robustness methods of LLMs perform in real-world noisy scenarios.
  arXiv  Detail & Related papers  (2023-10-10T10:22:05Z)
• Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion [54.33764537135906]
  VideoQA Transformer models demonstrate competitive performance on standard benchmarks. Do these models capture the rich multimodal structures and dynamics from video and text jointly? Are they achieving high scores by exploiting biases and spurious features?
  arXiv  Detail & Related papers  (2023-06-15T06:45:46Z)

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.