LLM In-Context Recall is Prompt Dependent
- URL: http://arxiv.org/abs/2404.08865v1
- Date: Sat, 13 Apr 2024 01:13:59 GMT
- Title: LLM In-Context Recall is Prompt Dependent
- Authors: Daniel Machlab, Rick Battle,
- Abstract summary: A model's ability to do this significantly influences its practical efficacy and dependability in real-world applications.
This study demonstrates that an LLM's recall capability is not only contingent upon the prompt's content but also may be compromised by biases in its training data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The proliferation of Large Language Models (LLMs) highlights the critical importance of conducting thorough evaluations to discern their comparative advantages, limitations, and optimal use cases. Particularly important is assessing their capacity to accurately retrieve information included in a given prompt. A model's ability to do this significantly influences how effectively it can utilize contextual details, thus impacting its practical efficacy and dependability in real-world applications. Our research analyzes the in-context recall performance of various LLMs using the needle-in-a-haystack method. In this approach, a factoid (the "needle") is embedded within a block of filler text (the "haystack"), which the model is asked to retrieve. We assess the recall performance of each model across various haystack lengths and with varying needle placements to identify performance patterns. This study demonstrates that an LLM's recall capability is not only contingent upon the prompt's content but also may be compromised by biases in its training data. Conversely, adjustments to model architecture, training strategy, or fine-tuning can improve performance. Our analysis provides insight into LLM behavior, offering direction for the development of more effective applications of LLMs.
Related papers
- Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment [56.87031484108484]
Large Language Models (LLMs) are increasingly recognized for their practical applications.
Retrieval-Augmented Generation (RAG) tackles this challenge and has shown a significant impact on LLMs.
By minimizing retrieval requests that yield neutral or harmful results, we can effectively reduce both time and computational costs.
arXiv Detail & Related papers (2024-11-09T15:12:28Z) - Evaluating Linguistic Capabilities of Multimodal LLMs in the Lens of Few-Shot Learning [15.919493497867567]
This study aims to evaluate the performance of Multimodal Large Language Models (MLLMs) on the VALSE benchmark.
We conducted a comprehensive assessment of state-of-the-art MLLMs, varying in model size and pretraining datasets.
arXiv Detail & Related papers (2024-07-17T11:26:47Z) - Comprehensive Reassessment of Large-Scale Evaluation Outcomes in LLMs: A Multifaceted Statistical Approach [64.42462708687921]
Evaluations have revealed that factors such as scaling, training types, architectures and other factors profoundly impact the performance of LLMs.
Our study embarks on a thorough re-examination of these LLMs, targeting the inadequacies in current evaluation methods.
This includes the application of ANOVA, Tukey HSD tests, GAMM, and clustering technique.
arXiv Detail & Related papers (2024-03-22T14:47:35Z) - Characterizing Truthfulness in Large Language Model Generations with
Local Intrinsic Dimension [63.330262740414646]
We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs)
We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
arXiv Detail & Related papers (2024-02-28T04:56:21Z) - LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges.
Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model.
This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
arXiv Detail & Related papers (2024-02-26T07:33:05Z) - Learning to Reduce: Optimal Representations of Structured Data in
Prompting Large Language Models [42.16047343029512]
Large Language Models (LLMs) have been widely used as general-purpose AI agents.
We propose a framework, Learning to Reduce, that fine-tunes a language model to generate a reduced version of an input context.
We show that our model achieves comparable accuracies in selecting the relevant evidence from an input context.
arXiv Detail & Related papers (2024-02-22T00:41:23Z) - C-ICL: Contrastive In-context Learning for Information Extraction [54.39470114243744]
c-ICL is a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations.
Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods.
arXiv Detail & Related papers (2024-02-17T11:28:08Z) - Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering.
The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored.
We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
arXiv Detail & Related papers (2023-12-26T07:24:46Z) - EpiK-Eval: Evaluation for Language Models as Epistemic Models [16.485951373967502]
We introduce EpiK-Eval, a novel question-answering benchmark tailored to evaluate LLMs' proficiency in formulating a coherent and consistent knowledge representation from segmented narratives.
We argue that these shortcomings stem from the intrinsic nature of prevailing training objectives.
The findings from this study offer insights for developing more robust and reliable LLMs.
arXiv Detail & Related papers (2023-10-23T21:15:54Z)
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.