Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost

• URL: http://arxiv.org/abs/2407.19825v1
• Date: Mon, 29 Jul 2024 09:21:52 GMT
• Title: Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost
• Authors: Sania Nayab, Giulio Rossolini, Giorgio Buttazzo, Nicolamaria Manes, Fabrizio Giacomelli,
• Abstract summary: This paper analyzes the impact of output lengths on large language models (LLMs) inference pipelines. It proposes novel metrics to evaluate them in terms of textitcorrect conciseness. It also examines the impact of controlling output length through a refined prompt engineering strategy, Constrained-CoT.
• Score: 4.299153274884264
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Today's large language models (LLMs) can solve challenging question-answering tasks, and prompt engineering techniques, such as chain-of-thought (CoT), have gained attention for enhancing the explanation and correctness of outputs. Nevertheless, models require significant time to generate answers augmented with lengthy reasoning details. To address this issue, this paper analyzes the impact of output lengths on LLM inference pipelines and proposes novel metrics to evaluate them in terms of \textit{correct conciseness}. It also examines the impact of controlling output length through a refined prompt engineering strategy, Constrained-CoT (CCoT), which encourages the model to limit output length. Experiments on pre-trained LLMs demonstrated the benefit of the proposed metrics and the effectiveness of CCoT across different models. For instance, constraining the reasoning of LLaMA2-70b to 100 words improves the accuracy from 36.01\% (CoT) to 41.07\% (CCoT) on the GSM8K dataset, while reducing the average output length by 28 words.

Related papers

• Short-Path Prompting in LLMs: Analyzing Reasoning Instability and Solutions for Robust Performance [33.16322104912836]
  Large language models' (LLMs) reasoning is largely due to the chain-of-thought (CoT) approaches. LLMs are instruction-tuned to provide long and detailed CoT pathways when responding to reasoning-related questions. Human beings are naturally cognitive misers and will prompt language models to give rather short responses.
  arXiv  Detail & Related papers  (2025-04-13T14:12:14Z)
• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models [54.04678363287392]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks. Recent advancements in OpenAI o1 and DeepSeek-R1 have further improved performance in System-2 reasoning domains.
  arXiv  Detail & Related papers  (2025-03-20T17:59:38Z)
• When More is Less: Understanding Chain-of-Thought Length in LLMs [53.77747102201451]
  Chain-of-thought (CoT) reasoning enhances the multi-step reasoning capabilities of large language models (LLMs) However, for most models and tasks, does an increase in CoT length consistently lead to improved reasoning accuracy? In this paper, we observe a nuanced relationship: as the number of reasoning steps increases, performance initially improves but eventually decreases.
  arXiv  Detail & Related papers  (2025-02-11T05:28:59Z)
• Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding [1.3479499607624648]
  Speculative decoding addresses bottleneck by introducing a two-stage framework: drafting and verification. A smaller, efficient model generates a preliminary draft, which is then refined by a larger, more sophisticated model. This paper provides a comprehensive survey of speculative decoding methods, categorizing them into draft-centric and model-centric approaches.
  arXiv  Detail & Related papers  (2024-11-20T09:46:30Z)
• Understanding Chain-of-Thought in LLMs through Information Theory [16.78730663293352]
  We formalize Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) through an information-theoretic lens. Specifically, our framework quantifies the information gain' at each reasoning step, enabling the identification of failure modes. We demonstrate the efficacy of our approach through extensive experiments on toy and GSM-8K data, where it significantly outperforms existing outcome-based methods.
  arXiv  Detail & Related papers  (2024-11-18T19:14:36Z)
• MetRex: A Benchmark for Verilog Code Metric Reasoning Using LLMs [2.0921175288836746]
  Large Language Models (LLMs) have been applied to various hardware design tasks, including Verilog code generation, EDA tool scripting, and RTL bug fixing. In this paper, we assess the ability of LLMs to reason about post-synthesis metrics of Verilog designs. We introduce MetRex, a large-scale dataset comprising 25,868 Verilog HDL designs and their corresponding post-synthesis metrics, namely area, delay, and static power.
  arXiv  Detail & Related papers  (2024-11-05T19:52:58Z)
• Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output [49.893971654861424]
  We present a light-weight approach for detecting nonfactual outputs from retrieval-augmented generation (RAG) We compute a factuality score that can be thresholded to yield a binary decision. Our experiments show high area under the ROC curve (AUC) across a wide range of relevant open source datasets.
  arXiv  Detail & Related papers  (2024-11-01T20:44:59Z)
• LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models [72.71150585370147]
  LongRecipe is an efficient training strategy for extending the context window of large language models. It simulates long-sequence inputs while maintaining training efficiency and significantly improves the model's understanding of long-range dependencies. LongRecipe can utilize long sequences while requiring only 30% of the target context window size, and reduces computational training resource over 85% compared to full sequence training.
  arXiv  Detail & Related papers  (2024-08-31T17:19:30Z)
• Optimizing Language Model's Reasoning Abilities with Weak Supervision [48.60598455782159]
  We present textscPuzzleBen, a weakly supervised benchmark that comprises 25,147 complex questions, answers, and human-generated rationales. A unique aspect of our dataset is the inclusion of 10,000 unannotated questions, enabling us to explore utilizing fewer supersized data to boost LLMs' inference capabilities.
  arXiv  Detail & Related papers  (2024-05-07T07:39:15Z)
• Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models [48.35385912526338]
  This paper explores the impact of extending input lengths on the capabilities of Large Language Models (LLMs) We isolate the effect of input length using multiple versions of the same sample, each being extended with padding of different lengths, types and locations. We show that the degradation trend appears in every version of our dataset, although at different intensities.
  arXiv  Detail & Related papers  (2024-02-19T16:04:53Z)
• CliqueParcel: An Approach For Batching LLM Prompts That Jointly Optimizes Efficiency And Faithfulness [13.554160815699435]
  CliqueParcel is designed to improve efficiency of large language models (LLMs) during the inference process. CliqueParcel is tested on eight widely recognized datasets. This work provides novel insights into inference efficiency and demonstrates promising performance.
  arXiv  Detail & Related papers  (2024-02-17T22:37:17Z)
• A Thorough Examination of Decoding Methods in the Era of LLMs [72.65956436513241]
  Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers. This paper provides a comprehensive and multifaceted analysis of various decoding methods within the context of large language models. Our findings reveal that decoding method performance is notably task-dependent and influenced by factors such as alignment, model size, and quantization.
  arXiv  Detail & Related papers  (2024-02-10T11:14:53Z)
• Extending Context Window of Large Language Models via Semantic Compression [21.35020344956721]
  Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses. We propose a novel semantic compression method that enables generalization to texts 6-8 times longer, without incurring significant computational costs or requiring fine-tuning.
  arXiv  Detail & Related papers  (2023-12-15T07:04:33Z)
• LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback [65.84061725174269]
  Recent large language models (LLM) are leveraging human feedback to improve their generation quality. We propose LLMRefine, an inference time optimization method to refine LLM's output. We conduct experiments on three text generation tasks, including machine translation, long-form question answering (QA), and topical summarization. LLMRefine consistently outperforms all baseline approaches, achieving improvements up to 1.7 MetricX points on translation tasks, 8.1 ROUGE-L on ASQA, 2.2 ROUGE-L on topical summarization.
  arXiv  Detail & Related papers  (2023-11-15T19:52:11Z)
• Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
  We make a systematic review of the literature, including the general methodology of supervised fine-tuning (SFT) We also review the potential pitfalls of SFT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies.
  arXiv  Detail & Related papers  (2023-08-21T15:35:16Z)
• To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
  Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs. We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting. Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
  arXiv  Detail & Related papers  (2023-05-22T17:02:15Z)
• Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline [22.08897444328099]
  Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs.
  arXiv  Detail & Related papers  (2023-05-22T15:36:06Z)
• Reducing Sequence Length by Predicting Edit Operations with Large Language Models [50.66922361766939]
  This paper proposes predicting edit spans for the source text for local sequence transduction tasks. We apply instruction tuning for Large Language Models on the supervision data of edit spans. Experiments show that the proposed method achieves comparable performance to the baseline in four tasks.
  arXiv  Detail & Related papers  (2023-05-19T17:51:05Z)
• LenAtten: An Effective Length Controlling Unit For Text Summarization [5.554982420311913]
  Fixed length summarization aims at generating summaries with a preset number of words or characters. Most recent researches incorporate length information with word embeddings as the input to the recurrent decoding unit. We present an effective length controlling unit Length Attention (LenAtten) to break this trade-off.
  arXiv  Detail & Related papers  (2021-06-01T08:45:41Z)

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.