Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models

• URL: http://arxiv.org/abs/2310.07301v2
• Date: Thu, 23 May 2024 09:32:52 GMT
• Title: Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models
• Authors: Yuchong Sun, Che Liu, Kun Zhou, Jinwen Huang, Ruihua Song, Wayne Xin Zhao, Fuzheng Zhang, Di Zhang, Kun Gai,
• Abstract summary: We introduce Parrot, a solution aiming to enhance multi-turn instruction following for large language models (LLMs) First, we introduce an efficient but effective method for collecting multi-turn instructions that feature human-like queries, such as anaphora and ellipsis. Second, we propose a context-aware preference optimization strategy to further enhance LLMs for complex queries in multi-turn interaction.
• Score: 79.32652077838046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans often interact with large language models (LLMs) in multi-turn interaction to obtain desired answers or more information. However, most existing studies overlook the multi-turn instruction following ability of LLMs, in terms of training dataset, training method, and evaluation benchmark. In this paper, we introduce Parrot, a solution aiming to enhance multi-turn instruction following for LLMs. First, we introduce an efficient but effective method for collecting multi-turn instructions that feature human-like queries, such as anaphora and ellipsis. Second, we propose a context-aware preference optimization strategy to further enhance LLMs for complex queries in multi-turn interaction. Moreover, to quantitatively evaluate LLMs in multi-turn instruction following, we manually build a multi-turn benchmark derived from existing ones. Extensive experiments show that Parrot improves current LLMs by up to 7.2% in multi-turn instruction following. Our dataset and codes will be open-sourced to facilitate future research.

Related papers

• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners [67.85635044939836]
  Large Language Models (LLMs) have shown impressive language capabilities. In this work, we investigate the spontaneous multilingual alignment improvement of LLMs. We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages.
  arXiv  Detail & Related papers  (2024-05-22T16:46:19Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• On the Performance of Multimodal Language Models [4.677125897916577]
  This study conducts a comparative analysis of different multimodal instruction tuning approaches. We reveal key insights for guiding architectural choices when incorporating multimodal capabilities into large language models.
  arXiv  Detail & Related papers  (2023-10-04T23:33:36Z)
• MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback [78.60644407028022]
  We introduce MINT, a benchmark that evaluates large language models' ability to solve tasks with multi-turn interactions. LLMs generally benefit from tools and language feedback, with performance gains of 1-8% for each turn of tool use. LLMs evaluated, supervised instruction-finetuning (SIFT) and reinforcement learning from human feedback (RLHF) generally hurt multi-turn capabilities.
  arXiv  Detail & Related papers  (2023-09-19T15:25:42Z)
• Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback [61.83548032416181]
  We present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages. Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
  arXiv  Detail & Related papers  (2023-07-29T18:01:46Z)
• Breaking Language Barriers with a LEAP: Learning Strategies for Polyglot LLMs [5.682384717239095]
  Large language models (LLMs) are at the forefront of transforming numerous domains globally. This paper tackles the imperative challenge of enhancing the multilingual performance of LLMs. We present novel techniques that unlock the true potential of LLMs in a polyglot landscape.
  arXiv  Detail & Related papers  (2023-05-28T14:48:38Z)

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.