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20202023
most citedA Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation

17 citations · 27 across the 15 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CL2023

Towards Controllable Natural Language Inference through Lexical Inference Types

Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann +1

Explainable natural language inference aims to provide a mechanism to produce explanatory (abductive) inference chains which ground claims to their supporting premises. A recent co…

cs.CL2023★ 1 cited

Estimating the Causal Effects of Natural Logic Features in Neural NLI Models

Julia Rozanova, Marco Valentino, Andre Freitas

Rigorous evaluation of the causal effects of semantic features on language model predictions can be hard to achieve for natural language reasoning problems. However, this is such a…

cs.CL2023

A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers

Jordan Meadows, Marco Valentino, Damien Teney +1

This paper proposes a methodology for generating and perturbing detailed derivations of equations at scale, aided by a symbolic engine, to evaluate the generalisability of Transfor…

cs.AI2023★ 17 cited

A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation

Xiaowei Huang, Wenjie Ruan, Wei Huang +14

Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many…

cs.CL2023

Interventional Probing in High Dimensions: An NLI Case Study

Julia Rozanova, Marco Valentino, Lucas Cordeiro +1

Probing strategies have been shown to detect the presence of various linguistic features in large language models; in particular, semantic features intermediate to the "natural log…