When Better Codebooks Are Not Enough: Predictive Performance and Behavioral Reliability in LLM Political Event Coding

• URL: http://arxiv.org/abs/2606.06781v1
• Date: Thu, 04 Jun 2026 23:51:14 GMT
• Title: When Better Codebooks Are Not Enough: Predictive Performance and Behavioral Reliability in LLM Political Event Coding
• Abstract summary: Many social-science studies rely on expert-written codebooks to turn text into structured data.<n>We study this problem in political event coding, a challenging source-target relation classification task.<n>We test whether expert codebooks become more effective when operationalized into LLM-friendly forms with clearer definitions.
• Score: 1.0312121265468142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High accuracy does not necessarily make an LLM a faithful coder. This issue matters because many social-science studies rely on expert-written codebooks to turn text into structured data. We study this problem in political event coding, a challenging source-target relation classification task beyond ordinary sentence-level classification, where models must determine what one actor did to another using detailed coding rules. We test whether expert codebooks become more effective when operationalized into LLM-friendly forms with clearer definitions, examples, retrieved context, and rules for difficult cases. We then evaluate behavioral reliability under controlled changes to label names, codebook order, and label-definition mappings. Clearer codebooks substantially improve classification performance, especially for fine-grained event classification. However, these predictive gains do not fully translate into behavioral reliability. Models may produce valid labels and recover definitions while still failing behavioral reliability tests under controlled codebook changes. These findings suggest that codebook-guided LLM systems should be evaluated not only by accuracy, but also by whether they preserve the coding logic that makes coded outputs meaningful for social-science research.

Related papers

• Task Abstention for Large Language Models in Code Generation [16.35286288670945]
  Large language models (LLMs) have revolutionized automated code generation.<n>One serious concern is the so-called hallucination''<n>We study the task abstention problem, i.e., determining whether a given LLM should abstain from performing a specific code generation task to avoid likely hallucination.
  arXiv  Detail & Related papers  (2026-05-16T14:58:11Z)
• Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study [0.0]
  This paper studies feedback-based automated verification of LLM-generated adaptation managers in Collective Adaptive Systems (CAS)<n>We focus on the key challenges of verification in the loop: how to detect failures of generated code at runtime and how to report them.<n>Our findings suggest that feedback precision is the dominant factor for reliable vibe coding in systems designed by domain experts with no programming skills.
  arXiv  Detail & Related papers  (2026-04-16T10:58:03Z)
• Are LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement [8.059802912761919]
  We uncover a systematic failure of large language models (LLMs) in matching code to natural language requirements.<n>More detailed prompt design, particularly with those requiring explanations and proposed corrections, leads to higher misjudgment rates.<n>We propose a Fix-guided Verification Filter that treats the model proposed fix as executable counterfactual evidence.
  arXiv  Detail & Related papers  (2026-02-28T08:35:25Z)
• Readability-Robust Code Summarization via Meta Curriculum Learning [53.44612630063336]
  In the real world, code is often poorly structured or obfuscated, significantly degrading model performance.<n>We propose RoFTCodeSum, a novel fine-tuning method that enhances the robustness of code summarization against poorly readable code.
  arXiv  Detail & Related papers  (2026-01-09T02:38:24Z)
• From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence [150.3696990310269]
  Large language models (LLMs) have transformed automated software development by enabling direct translation of natural language descriptions into functional code.<n>We provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs.<n>We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder)
  arXiv  Detail & Related papers  (2025-11-23T17:09:34Z)
• Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications [0.6813925418351435]
  Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks.<n>In this paper, we uncover a systematic failure of LLMs in evaluating whether code aligns with natural language requirements.<n>Our results reveal that LLMs frequently misclassify correct code implementations as either not satisfying requirements'' or containing potential defects.
  arXiv  Detail & Related papers  (2025-08-17T13:07:26Z)
• Is Compression Really Linear with Code Intelligence? [60.123628177110206]
  textitFormat Annealing is a lightweight, transparent training methodology designed to assess the intrinsic capabilities of pre-trained models equitably.<n>Our empirical results reveal a fundamental logarithmic relationship between measured code intelligence and bits-per-character (BPC)<n>Our work provides a more nuanced understanding of compression's role in developing code intelligence and contributes a robust evaluation framework in the code domain.
  arXiv  Detail & Related papers  (2025-05-16T16:59:14Z)
• Memorize or Generalize? Evaluating LLM Code Generation with Code Rewriting [54.48306552577881]
  We argue that large language models (LLMs) are mostly doing memorization (i.e., replicating or reusing large parts of their training data) versus generalization.<n>Existing evaluations largely proxy neglecting surface/structural similarity, thereby conflating benign reuse of repeated code with harmful recall and memorization task correctness.<n>We propose Memorization Risk Index (MRI), a normalized score that combines two signals: (i) how similar the model's answer for the rewritten task is to the original ground-truth solution, and (ii) how much performance drops from the original task to its rewritten counterpart.
  arXiv  Detail & Related papers  (2025-03-04T05:39:24Z)
• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [76.59316249991657]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems.<n>While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited.<n>We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates [46.74037090843497]
  Large Language Models (LLMs) are transforming the way developers approach programming by automatically generating code based on natural language descriptions. This paper puts forward RankEF, an innovative approach for code ranking that leverages execution feedback. Experiments on three code generation benchmarks demonstrate that RankEF significantly outperforms the state-of-the-art CodeRanker.
  arXiv  Detail & Related papers  (2024-08-26T01:48:57Z)
• Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Concepts [7.005758904228446]
  We gather and curate three real-world political science codebooks -- covering protest events, political violence and manifestos.<n>We propose a five-stage framework for codebook-LLM measurement: preparing a codebook for both humans and LLMs, testing LLMs' basic capabilities on a codebook, evaluating zero-shot measurement accuracy.<n>We find current open-weight LLMs have limitations in following codebooks zero-shot, but that supervised instruction tuning can substantially improve performance.
  arXiv  Detail & Related papers  (2024-07-15T14:20:09Z)

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.