Review-Feedback-Reason (ReFeR): A Novel Framework for NLG Evaluation and Reasoning

Assessing the quality of Natural Language Generation (NLG) outputs, such as those produced by large language models (LLMs), poses significant challenges. Traditional approaches involve either resource-intensive human evaluations or automatic metrics, which often exhibit a low correlation with human judgment. In this study, we propose Review-Feedback-Reason (ReFeR), a novel evaluation framework for NLG using LLM agents. We rigorously test ReFeR using two pre-existing benchmark datasets on diverse NLG tasks. The proposed framework not only enhances the accuracy of NLG evaluation, surpassing previous benchmarks by $\sim$20\%, but also generates constructive feedback and significantly improves collective reasoning. This feedback is then leveraged for the creation of instruction-tuning datasets, which, when used to fine-tune smaller models like Mistral-7B, makes them extremely good evaluators, yielding a better correlation with human evaluations and performance nearly on par with GPT-3.5. We highlight the effectiveness of our methodology through its application on three reasoning benchmarks, where it outperforms most of the state-of-the-art methods, and also outperforms the reasoning capabilities of models like GPT-3.5 Turbo by $\sim$11.67\% and GPT-4 by $\sim$1\% on an average.

DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit

Multi-component compounding is a prevalent phenomenon in Sanskrit, and understanding the implicit structure of a compound's components is crucial for deciphering its meaning. Earlier approaches in Sanskrit have focused on binary compounds and neglected the multi-component compound setting. This work introduces the novel task of nested compound type identification (NeCTI), which aims to identify nested spans of a multi-component compound and decode the implicit semantic relations between them. To the best of our knowledge, this is the first attempt in the field of lexical semantics to propose this task. We present 2 newly annotated datasets including an out-of-domain dataset for this task. We also benchmark these datasets by exploring the efficacy of the standard problem formulations such as nested named entity recognition, constituency parsing and seq2seq, etc. We present a novel framework named DepNeCTI: Dependency-based Nested Compound Type Identifier that surpasses the performance of the best baseline with an average absolute improvement of 13.1 points F1-score in terms of Labeled Span Score (LSS) and a 5-fold enhancement in inference efficiency. In line with the previous findings in the binary Sanskrit compound identification task, context provides benefits for the NeCTI task. The codebase and datasets are publicly available at: https://github.com/yaswanth-iitkgp/DepNeCTI