Methods for Estimating and Improving Robustness of Language Models
- URL: http://arxiv.org/abs/2206.08446v1
- Date: Thu, 16 Jun 2022 21:02:53 GMT
- Title: Methods for Estimating and Improving Robustness of Language Models
- Authors: Michal \v{S}tef\'anik
- Abstract summary: Large language models (LLMs) suffer notorious flaws related to their preference for simple, surface-level textual relations over full semantic complexity.
This proposal investigates a common denominator of this problem in their weak ability to generalise outside of the training domain.
We find that incorporating some of these measures in the training objectives leads to enhanced distributional robustness of neural models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Despite their outstanding performance, large language models (LLMs) suffer
notorious flaws related to their preference for simple, surface-level textual
relations over full semantic complexity of the problem. This proposal
investigates a common denominator of this problem in their weak ability to
generalise outside of the training domain. We survey diverse research
directions providing estimations of model generalisation ability and find that
incorporating some of these measures in the training objectives leads to
enhanced distributional robustness of neural models. Based on these findings,
we present future research directions towards enhancing the robustness of LLMs.
Related papers
- Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data [76.90128359866462]
We investigate the interplay between generalization and memorization in large language models at scale.
With various sizes of open-source LLMs and their pretraining corpora, we observe that as the model size increases, the task-relevant $n$-gram pair data becomes increasingly important.
Our results support the hypothesis that LLMs' capabilities emerge from a delicate balance of memorization and generalization with sufficient task-related pretraining data.
arXiv Detail & Related papers (2024-07-20T21:24:40Z) - Predicting and Understanding Human Action Decisions: Insights from Large Language Models and Cognitive Instance-Based Learning [0.0]
Large Language Models (LLMs) have demonstrated their capabilities across various tasks.
This paper exploits the reasoning and generative capabilities of the LLMs to predict human behavior in two sequential decision-making tasks.
We compare the performance of LLMs with a cognitive instance-based learning model, which imitates human experiential decision-making.
arXiv Detail & Related papers (2024-07-12T14:13:06Z) - Investigating the Role of Instruction Variety and Task Difficulty in Robotic Manipulation Tasks [50.75902473813379]
This work introduces a comprehensive evaluation framework that systematically examines the role of instructions and inputs in the generalisation abilities of such models.
The proposed framework uncovers the resilience of multimodal models to extreme instruction perturbations and their vulnerability to observational changes.
arXiv Detail & Related papers (2024-07-04T14:36:49Z) - Chain-of-Thought Prompting for Demographic Inference with Large Multimodal Models [58.58594658683919]
Large multimodal models (LMMs) have shown transformative potential across various research tasks.
Our findings indicate LMMs possess advantages in zero-shot learning, interpretability, and handling uncurated 'in-the-wild' inputs.
We propose a Chain-of-Thought augmented prompting approach, which effectively mitigates the off-target prediction issue.
arXiv Detail & Related papers (2024-05-24T16:26:56Z) - RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs [49.386699863989335]
Training large language models (LLMs) to serve as effective assistants for humans requires careful consideration.
A promising approach is reinforcement learning from human feedback (RLHF), which leverages human feedback to update the model in accordance with human preferences.
In this paper, we analyze RLHF through the lens of reinforcement learning principles to develop an understanding of its fundamentals.
arXiv Detail & Related papers (2024-04-12T15:54:15Z) - Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey [25.732397636695882]
Large language models (LLMs) have recently shown impressive performance on tasks involving reasoning.
Despite these successes, the depth of LLMs' reasoning abilities remains uncertain.
arXiv Detail & Related papers (2024-04-02T11:46:31Z) - Large Language Models for Forecasting and Anomaly Detection: A
Systematic Literature Review [10.325003320290547]
This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection.
LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains.
This review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, issues with generalizability across different contexts, and the phenomenon of model hallucinations.
arXiv Detail & Related papers (2024-02-15T22:43:02Z) - Improving Open Information Extraction with Large Language Models: A
Study on Demonstration Uncertainty [52.72790059506241]
Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text.
Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
arXiv Detail & Related papers (2023-09-07T01:35:24Z) - Post Hoc Explanations of Language Models Can Improve Language Models [43.2109029463221]
We present a novel framework, Amplifying Model Performance by Leveraging In-Context Learning with Post Hoc Explanations (AMPLIFY)
We leverage post hoc explanation methods which output attribution scores (explanations) capturing the influence of each of the input features on model predictions.
Our framework, AMPLIFY, leads to prediction accuracy improvements of about 10-25% over a wide range of tasks.
arXiv Detail & Related papers (2023-05-19T04:46:04Z) - Competence-Based Analysis of Language Models [24.09077801383941]
Large, pretrained neural language models (LLMs) can be alarmingly brittle to small changes in inputs or application contexts.
Our framework, CALM, establishes the first quantitative measure of LLM competence.
We develop a novel approach for performing causal probing interventions using gradient-based adversarial attacks.
arXiv Detail & Related papers (2023-03-01T08:53:36Z) - GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision.
We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
arXiv Detail & Related papers (2023-02-16T16:45:36Z)
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.