Measuring the Groundedness of Legal Question-Answering Systems
- URL: http://arxiv.org/abs/2410.08764v1
- Date: Fri, 11 Oct 2024 12:23:45 GMT
- Title: Measuring the Groundedness of Legal Question-Answering Systems
- Authors: Dietrich Trautmann, Natalia Ostapuk, Quentin Grail, Adrian Alan Pol, Guglielmo Bonifazi, Shang Gao, Martin Gajek,
- Abstract summary: In high-stakes domains like legal question-answering, the accuracy and trustworthiness of generative AI systems are of paramount importance.
This work presents a benchmark of various methods to assess the groundedness of AI-generated responses, aiming to significantly enhance their reliability.
- Score: 2.3179590896468327
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In high-stakes domains like legal question-answering, the accuracy and trustworthiness of generative AI systems are of paramount importance. This work presents a comprehensive benchmark of various methods to assess the groundedness of AI-generated responses, aiming to significantly enhance their reliability. Our experiments include similarity-based metrics and natural language inference models to evaluate whether responses are well-founded in the given contexts. We also explore different prompting strategies for large language models to improve the detection of ungrounded responses. We validated the effectiveness of these methods using a newly created grounding classification corpus, designed specifically for legal queries and corresponding responses from retrieval-augmented prompting, focusing on their alignment with source material. Our results indicate potential in groundedness classification of generated responses, with the best method achieving a macro-F1 score of 0.8. Additionally, we evaluated the methods in terms of their latency to determine their suitability for real-world applications, as this step typically follows the generation process. This capability is essential for processes that may trigger additional manual verification or automated response regeneration. In summary, this study demonstrates the potential of various detection methods to improve the trustworthiness of generative AI in legal settings.
Related papers
- Likelihood as a Performance Gauge for Retrieval-Augmented Generation [78.28197013467157]
We show that likelihoods serve as an effective gauge for language model performance.
We propose two methods that use question likelihood as a gauge for selecting and constructing prompts that lead to better performance.
arXiv Detail & Related papers (2024-11-12T13:14:09Z) - Evaluating the Efficacy of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-Tuning Decision-Making [1.3812010983144802]
This study evaluates large language models (LLMs) across diverse domains, including cybersecurity, medicine, and finance.
The results indicate that model size and types of prompts used for inference significantly influenced response length and quality.
arXiv Detail & Related papers (2024-06-25T20:52:31Z) - Word-Level ASR Quality Estimation for Efficient Corpus Sampling and
Post-Editing through Analyzing Attentions of a Reference-Free Metric [5.592917884093537]
The potential of quality estimation (QE) metrics is introduced and evaluated as a novel tool to enhance explainable artificial intelligence (XAI) in ASR systems.
The capabilities of the NoRefER metric are explored in identifying word-level errors to aid post-editors in refining ASR hypotheses.
arXiv Detail & Related papers (2024-01-20T16:48:55Z) - From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
We discuss a paradigm shift from static evaluation methods to adaptive testing.
This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time.
We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
arXiv Detail & Related papers (2023-06-18T09:54:33Z) - A Call to Reflect on Evaluation Practices for Failure Detection in Image
Classification [0.491574468325115]
We present a large-scale empirical study for the first time enabling benchmarking confidence scoring functions.
The revelation of a simple softmax response baseline as the overall best performing method underlines the drastic shortcomings of current evaluation.
arXiv Detail & Related papers (2022-11-28T12:25:27Z) - Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue
Systems [71.33737787564966]
End-to-end (E2E) task-oriented dialogue (ToD) systems are prone to fall into the so-called 'likelihood trap'
We propose a reranking method which aims to select high-quality items from the lists of responses initially overgenerated by the system.
Our methods improve a state-of-the-art E2E ToD system by 2.4 BLEU, 3.2 ROUGE, and 2.8 METEOR scores, achieving new peak results.
arXiv Detail & Related papers (2022-11-07T15:59:49Z) - Reinforcement Learning with Heterogeneous Data: Estimation and Inference [84.72174994749305]
We introduce the K-Heterogeneous Markov Decision Process (K-Hetero MDP) to address sequential decision problems with population heterogeneity.
We propose the Auto-Clustered Policy Evaluation (ACPE) for estimating the value of a given policy, and the Auto-Clustered Policy Iteration (ACPI) for estimating the optimal policy in a given policy class.
We present simulations to support our theoretical findings, and we conduct an empirical study on the standard MIMIC-III dataset.
arXiv Detail & Related papers (2022-01-31T20:58:47Z) - Efficient falsification approach for autonomous vehicle validation using
a parameter optimisation technique based on reinforcement learning [6.198523595657983]
The widescale deployment of Autonomous Vehicles (AV) appears to be imminent despite many safety challenges that are yet to be resolved.
The uncertainties in the behaviour of the traffic participants and the dynamic world cause reactions in advanced autonomous systems.
This paper presents an efficient falsification method to evaluate the System Under Test.
arXiv Detail & Related papers (2020-11-16T02:56:13Z) - PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative
Dialogue Systems [48.99561874529323]
There are three kinds of automatic methods to evaluate the open-domain generative dialogue systems.
Due to the lack of systematic comparison, it is not clear which kind of metrics are more effective.
We propose a novel and feasible learning-based metric that can significantly improve the correlation with human judgments.
arXiv Detail & Related papers (2020-04-06T04:36:33Z) - Interpretable Off-Policy Evaluation in Reinforcement Learning by
Highlighting Influential Transitions [48.91284724066349]
Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education.
Traditional measures such as confidence intervals may be insufficient due to noise, limited data and confounding.
We develop a method that could serve as a hybrid human-AI system, to enable human experts to analyze the validity of policy evaluation estimates.
arXiv Detail & Related papers (2020-02-10T00:26:43Z)
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.