Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning
- URL: http://arxiv.org/abs/2404.16807v2
- Date: Fri, 27 Sep 2024 14:50:59 GMT
- Title: Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning
- Authors: Tianhui Zhang, Bei Peng, Danushka Bollegala,
- Abstract summary: Generative Commonsense Reasoning (GCR) requires a model to reason about a situation using commonsense knowledge.
The diversity of the generation is equally important because it reflects the model's ability to use a range of commonsense knowledge facts.
We propose a simple method that diversifies the LLM generations, while preserving their quality.
- Score: 28.654890118684957
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generative Commonsense Reasoning (GCR) requires a model to reason about a situation using commonsense knowledge, while generating coherent sentences. Although the quality of the generated sentences is crucial, the diversity of the generation is equally important because it reflects the model's ability to use a range of commonsense knowledge facts. Large Language Models (LLMs) have shown proficiency in enhancing the generation quality across various tasks through in-context learning (ICL) using given examples without the need for any fine-tuning. However, the diversity aspect in LLM outputs has not been systematically studied before. To address this, we propose a simple method that diversifies the LLM generations, while preserving their quality. Experimental results on three benchmark GCR datasets show that our method achieves an ideal balance between the quality and diversity. Moreover, the sentences generated by our proposed method can be used as training data to improve diversity in existing commonsense generators.
Related papers
- Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks.
Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval.
This paper proposes a unified approach that integrates the strengths of both paradigms.
arXiv Detail & Related papers (2024-11-01T01:51:31Z) - Unveiling the Generalization Power of Fine-Tuned Large Language Models [81.70754292058258]
We investigate whether fine-tuning affects the intrinsic generalization ability intrinsic to Large Language Models (LLMs)
Our main findings reveal that models fine-tuned on generation and classification tasks exhibit dissimilar behaviors in generalizing to different domains and tasks.
We observe that integrating the in-context learning strategy during fine-tuning on generation tasks can enhance the model's generalization ability.
arXiv Detail & Related papers (2024-03-14T08:18:59Z) - Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral.
This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
arXiv Detail & Related papers (2024-02-16T13:53:26Z) - Improving Diversity of Demographic Representation in Large Language
Models via Collective-Critiques and Self-Voting [19.79214899011072]
This paper formalizes diversity of representation in generative large language models.
We present evaluation datasets and propose metrics to measure diversity in generated responses along people and culture axes.
We find that LLMs understand the notion of diversity, and that they can reason and critique their own responses for that goal.
arXiv Detail & Related papers (2023-10-25T10:17:17Z) - Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning [57.74233319453229]
Large language models (LLMs) have emerged as a groundbreaking technology and their unparalleled text generation capabilities have sparked interest in their application to the fundamental sentence representation learning task.
We propose MultiCSR, a multi-level contrastive sentence representation learning framework that decomposes the process of prompting LLMs to generate a corpus.
Our experiments reveal that MultiCSR enables a less advanced LLM to surpass the performance of ChatGPT, while applying it to ChatGPT achieves better state-of-the-art results.
arXiv Detail & Related papers (2023-10-17T03:21:43Z) - UniDiff: Advancing Vision-Language Models with Generative and
Discriminative Learning [86.91893533388628]
This paper presents UniDiff, a unified multi-modal model that integrates image-text contrastive learning (ITC), text-conditioned image synthesis learning (IS), and reciprocal semantic consistency modeling (RSC)
UniDiff demonstrates versatility in both multi-modal understanding and generative tasks.
arXiv Detail & Related papers (2023-06-01T15:39:38Z) - Semantic Diversity in Dialogue with Natural Language Inference [19.74618235525502]
This paper makes two substantial contributions to improving diversity in dialogue generation.
First, we propose a novel metric which uses Natural Language Inference (NLI) to measure the semantic diversity of a set of model responses for a conversation.
Second, we demonstrate how to iteratively improve the semantic diversity of a sampled set of responses via a new generation procedure called Diversity Threshold Generation.
arXiv Detail & Related papers (2022-05-03T13:56:32Z) - Diverse Keyphrase Generation with Neural Unlikelihood Training [6.645227801791013]
We study sequence-to-sequence (S2S) keyphrase generation models from the perspective of diversity.
We first analyze the extent of information redundancy present in the outputs generated by a baseline model trained using maximum likelihood estimation (MLE)
arXiv Detail & Related papers (2020-10-15T11:12:26Z) - Informed Sampling for Diversity in Concept-to-Text NLG [8.883733362171034]
We propose an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce.
Specifically, we augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output.
arXiv Detail & Related papers (2020-04-29T17:43:24Z) - Self-Adversarial Learning with Comparative Discrimination for Text
Generation [111.18614166615968]
We propose a novel self-adversarial learning (SAL) paradigm for improving GANs' performance in text generation.
During training, SAL rewards the generator when its currently generated sentence is found to be better than its previously generated samples.
Experiments on text generation benchmark datasets show that our proposed approach substantially improves both the quality and the diversity.
arXiv Detail & Related papers (2020-01-31T07:50:25Z)
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.