Summary of Faitheval: Can Your Language Model Stay Faithful to Context, Even If “the Moon Is Made Of Marshmallows”, by Yifei Ming et al.
FaithEval: Can Your Language Model Stay Faithful to Context, Even If “The Moon is Made of Marshmallows”
by Yifei Ming, Senthil Purushwalkam, Shrey Pandit, Zixuan Ke, Xuan-Phi Nguyen, Caiming Xiong, Shafiq Joty
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed FaithEval benchmark evaluates the faithfulness of large language models (LLMs) and retrieval-augmented generation (RAG) systems in contextual scenarios. This is crucial for reliable deployment in real-world applications, as incorrect or unsupported information can erode user trust. The benchmark consists of 4.9K high-quality problems across three diverse tasks: unanswerable, inconsistent, and counterfactual contexts. These tasks simulate real-world challenges where retrieval mechanisms may surface incomplete, contradictory, or fabricated information. FaithEval employs a rigorous four-stage context construction and validation framework, validated through both LLM-based auto-evaluation and human validation. The study reveals that even state-of-the-art models often struggle to remain faithful to the given context, and that larger models do not necessarily exhibit improved faithfulness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) and retrieval-augmented generation (RAG) systems are essential for many applications. However, they can generate responses that are misaligned with the provided context. This is a significant challenge, as incorrect or unsupported information can erode user trust. To address this issue, researchers have developed FaithEval, a novel benchmark that evaluates the faithfulness of LLMs in contextual scenarios. The benchmark consists of three diverse tasks: unanswerable, inconsistent, and counterfactual contexts. These tasks simulate real-world challenges where retrieval mechanisms may surface incomplete, contradictory, or fabricated information. FaithEval is an essential tool for developing reliable LLMs and RAG systems. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
