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arxiv: 2606.25747 · v2 · pith:LQE6WVJNnew · submitted 2026-06-24 · 💻 cs.SE

CodeChat-Eval: Evaluating Large Language Models in Multi-Turn Code Refinement Dialogues

Guoxiang Guo , Kla Tantithamthavorn , Neelofar Neelofar , Yuanyuan Qi , Aldeida Aleti This is my paper

Pith reviewed 2026-06-25 20:20 UTC · model grok-4.3

classification 💻 cs.SE
keywords code refinementmulti-turn dialoguefunctional correctnessLLM evaluationsoftware engineering benchmarkscode generationdialogue systemsrefinement dialogues
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The pith

Large language models lose substantial functional correctness when refining code across multiple dialogue turns.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CodeChat-Eval, a framework that builds multi-turn refinement dialogues from initial code tasks using a dynamic instruction selection algorithm. Existing single-turn benchmarks cannot check whether follow-up requests for style improvements, restructuring, or strategy changes still produce code that passes the original test suite. Evaluation across open-weight and proprietary models shows statistically significant correctness drops from 19.2 percent to 69.2 percent, largest on logic-level and additive changes. A reader would care because real developer work consists of iterative refinements rather than isolated generations, so current tests overstate model reliability in practice. If the claim holds, benchmarks must shift to multi-turn settings to measure whether models preserve intended behavior through dialogue.

Core claim

CodeChat-Eval constructs evaluation sessions from multi-turn code refinement dialogues using a dynamic instruction selection algorithm; empirical results on open-weight and proprietary LLMs show statistically significant decreases in functional correctness from 19.2 percent for GPT-5 Nano to 69.2 percent for Llama 3.1 8B, with the largest drops tied to logic-level refinements and additive change requests.

What carries the argument

Dynamic instruction selection algorithm that generates multi-turn refinement dialogues from base tasks while retaining the original task test suites to detect losses in functional correctness.

If this is right

  • Evaluation of code-generating LLMs must incorporate multi-turn refinement rather than single-turn generation to reflect actual usage.
  • Logic-level refinements and additive change requests require targeted improvements because they produce the largest losses in correctness.
  • Both proprietary and open-weight models exhibit the problem, indicating it is not solved by scale or proprietary training alone.
  • New benchmarks are needed that test functionality-preserving refinement beyond initial code generation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Development teams may need to add verification steps after each refinement turn instead of accepting model output at face value.
  • Training regimes could incorporate synthetic multi-turn refinement examples to reduce the observed correctness erosion.
  • The same dialogue-construction method could be applied to evaluate iterative tasks outside code, such as multi-turn debugging or specification refinement.

Load-bearing premise

The dialogues produced by the dynamic instruction selection algorithm match the distribution and difficulty of real developer interactions, and the original test suites remain sufficient to detect any loss of intended behavior after refinements.

What would settle it

Measuring the same models on a collection of logged, real-world multi-turn refinement sessions from open-source projects and checking whether the correctness drops match those observed in the constructed dialogues.

Figures

Figures reproduced from arXiv: 2606.25747 by Aldeida Aleti, Guoxiang Guo, Kla Tantithamthavorn, Neelofar Neelofar, Yuanyuan Qi.

Figure 1
Figure 1. Figure 1: A motivating example of multi-turn code refinement that demonstrates [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: CodeChat-Eval overview. Instruction Filtering: During the inspection of instructions, we observe that certain code refinement instructions con￾flict with functional correctness evaluation harness. Modern functional correctness benchmarks, e.g., EvalPlus [19] and BigCodeBench [9], rely on a rigid test harness that invokes code via fixed function signatures. Consequently, instructions that explicitly request… view at source ↗
Figure 3
Figure 3. Figure 3: (RQ1) The decrease in functional correctness across multi-turn code [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (RQ2.1) The regression rate of different scopes. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (RQ2.2) The regression rate of different changes. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Instruction adherence rate across multi-turn code refinement. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: (Discussion C) The self-correction in multi-turn code refinement. (a) [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
read the original abstract

Large Language Models (LLMs) are increasingly used in software engineering to generate and refine code. In practice, developers often continue from an initial code generation request with follow-up refinement instructions, such as requests to improve style, restructure implementation, or change the execution strategy while preserving the intended behaviour. However, existing benchmarks generally omit this multi-turn code refinement dialogue setting and therefore cannot evaluate whether LLMs maintain functional correctness, i.e., whether the refined code still passes the test suite for the original task. To address this limitation, we introduce CodeChat-Eval, an evaluation framework that constructs evaluation sessions from multi-turn code refinement dialogues using a dynamic instruction selection algorithm. Our empirical study on open-weight and proprietary LLMs observes a statistically significant decrease ranging from 19.2% (GPT-5 Nano) to 69.2% (Llama 3.1 8B) in functional correctness over multi-turn refinement. The largest correctness drops are associated with logic-level refinements and additive change requests. These findings indicate that LLMs struggle to maintain functional correctness during multi-turn code refinement dialogues, and highlight the need for benchmarks that evaluate functionality-preserving refinement beyond single-turn generation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper introduces CodeChat-Eval, a framework that uses a dynamic instruction selection algorithm to generate multi-turn code refinement dialogues from initial tasks. It evaluates open-weight and proprietary LLMs and reports statistically significant drops in functional correctness (19.2% for GPT-5 Nano to 69.2% for Llama 3.1 8B) over refinement turns, with the largest drops linked to logic-level refinements and additive change requests. The central claim is that existing single-turn benchmarks miss this setting and that LLMs struggle to maintain correctness in multi-turn refinement.

Significance. If the synthetic dialogues produced by the dynamic instruction selection algorithm match the distribution, specificity, and difficulty of real developer refinement sessions, the results would demonstrate a practically important limitation of current LLMs for iterative software engineering workflows and would motivate the development of functionality-preserving refinement techniques and more realistic benchmarks.

major comments (3)
  1. [Methods / dynamic instruction selection algorithm] The headline result (statistically significant correctness drops of 19.2–69.2 %) rests on the assumption that the dynamic instruction selection algorithm produces refinement dialogues whose distribution and difficulty match real developer interactions. No human validation study, comparison to logged developer sessions, or distributional statistics on request frequencies/specificity are described, making it impossible to rule out that the measured degradation is an artifact of over-sampling logic-level or additive changes.
  2. [Evaluation setup / test suites] The abstract states that the original task test suites remain sufficient to detect loss of intended behavior after refinements, but provides no details on test-suite construction, coverage, or controls for prompt length. This is load-bearing for interpreting the reported drops as genuine losses of functional correctness rather than artifacts of incomplete oracles.
  3. [Results / category analysis] The paper associates the largest drops with logic-level refinements and additive requests, yet the abstract and available description give no breakdown of how refinement categories were labeled, inter-annotator agreement, or controls for confounding factors such as dialogue length or model-specific prompt sensitivity.
minor comments (1)
  1. [Abstract] The abstract mentions 'statistically significant' drops but does not name the statistical test, correction for multiple comparisons, or effect-size reporting; these details should be added for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which identify key areas where additional detail and validation would strengthen the manuscript. We respond to each major comment below and will revise the paper accordingly.

read point-by-point responses

  1. Referee: [Methods / dynamic instruction selection algorithm] The headline result (statistically significant correctness drops of 19.2–69.2 %) rests on the assumption that the dynamic instruction selection algorithm produces refinement dialogues whose distribution and difficulty match real developer interactions. No human validation study, comparison to logged developer sessions, or distributional statistics on request frequencies/specificity are described, making it impossible to rule out that the measured degradation is an artifact of over-sampling logic-level or additive changes.

    Authors: We agree that explicit validation against real developer distributions would increase confidence in the results. Section 3.2 describes the dynamic instruction selection algorithm, which samples from a curated instruction pool to ensure coverage of refinement types while avoiding repetition. We did not perform a human validation study or direct comparison to logged sessions. In revision we will add distributional statistics on the generated dialogues (e.g., frequency of logic-level vs. style changes), a limitations subsection discussing fidelity to real workflows, and, if feasible within the revision timeline, a small-scale human annotation study to assess perceived realism. revision: yes

  2. Referee: [Evaluation setup / test suites] The abstract states that the original task test suites remain sufficient to detect loss of intended behavior after refinements, but provides no details on test-suite construction, coverage, or controls for prompt length. This is load-bearing for interpreting the reported drops as genuine losses of functional correctness rather than artifacts of incomplete oracles.

    Authors: The test suites are taken directly from the source benchmarks (HumanEval, MBPP, and APPS) and were not modified. We will revise the evaluation-setup section to report test-suite sizes, statement and branch coverage where available from the original datasets, and any length-normalization steps applied to prompts. This will make explicit that the oracles remain unchanged and that observed drops are measured against the same functional requirements. revision: yes

  3. Referee: [Results / category analysis] The paper associates the largest drops with logic-level refinements and additive requests, yet the abstract and available description give no breakdown of how refinement categories were labeled, inter-annotator agreement, or controls for confounding factors such as dialogue length or model-specific prompt sensitivity.

    Authors: Category labels were assigned by mapping each selected instruction to one of four predefined change types (logic, additive, style, structural) according to the instruction taxonomy in Section 3.1. We will expand the results section with the exact labeling rules, report inter-annotator agreement if a second annotator was used, and add regression controls for dialogue length and model to isolate the effect of category. These additions will support the claim that logic-level and additive refinements drive the largest drops. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical measurement on constructed benchmark

full rationale

The paper's central claim is an observed drop in functional correctness (19.2–69.2%) measured by executing refined code against original test suites. This is a direct empirical result from running LLMs on dialogues generated by the described algorithm; it does not reduce to a fitted parameter, self-definition, or self-citation chain. The dynamic instruction selection algorithm is presented as a construction method rather than a derived prediction, and no equations or uniqueness theorems are invoked. Validity concerns about realism of the synthetic dialogues are external to circularity analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities can be extracted. The framework implicitly assumes that test suites capture intended behavior after arbitrary refinements and that the dynamic selection produces representative dialogues.

pith-pipeline@v0.9.1-grok · 5765 in / 1079 out tokens · 21570 ms · 2026-06-25T20:20:38.067563+00:00 · methodology

discussion (0)

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