Skip to content
OpenTrain AIFor AI Companies

HFEPX · Eval paper review

Training with Pseudo-Code for Instruction Following

Prince Kumar, Rudra Murthy, Riyaz Bhat, Danish Contractor

Published

May 23, 2025

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Mixed

Signals refreshed

Mar 11, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Coding Demonstrations Math

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Despite rapid advances in the capabilities of Large Language Models (LLMs), they continue to struggle with following relatively simple and unambiguous instructions, particularly when compositional structure is involved. Recent work suggests that models may follow instructions more effectively when they are expressed in pseudo-code rather than natural language. However, writing pseudo-code programs can be tedious, and relying on few-shot demonstrations or inference-time code prompting is often unnatural for non-expert users of LLMs. To overcome these limitations, we propose a training time approach that fine-tunes LLMs using instruction-tuning data augmented with pseudo-code representations of natural language instructions paired with final responses. We evaluate our method on 12 publicly available benchmarks spanning instruction-following, mathematical reasoning, and commonsense reasoning, across six base models. Our results show that models trained with pseudo-code follow instructions more reliably, achieving relative gains of 8-21\% on instruction following benchmarks, while largely preserving and in some cases improving performance on mathematical and commonsense reasoning tasks, with an average gain of up to 30\% across all evaluated benchmarks.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

partial

Demonstrations

Directly usable for protocol triage.

"However, writing pseudo-code programs can be tedious, and relying on few-shot demonstrations or inference-time code prompting is often unnatural for non-expert users of LLMs."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Despite rapid advances in the capabilities of Large Language Models (LLMs), they continue to struggle with following relatively simple and unambiguous instructions, particularly when compositional structure is involved."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Despite rapid advances in the capabilities of Large Language Models (LLMs), they continue to struggle with following relatively simple and unambiguous instructions, particularly when compositional structure is involved."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Despite rapid advances in the capabilities of Large Language Models (LLMs), they continue to struggle with following relatively simple and unambiguous instructions, particularly when compositional structure is involved."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Despite rapid advances in the capabilities of Large Language Models (LLMs), they continue to struggle with following relatively simple and unambiguous instructions, particularly when compositional structure is involved."

Rater Population

partial

Mixed

Helpful for staffing comparability.

"However, writing pseudo-code programs can be tedious, and relying on few-shot demonstrations or inference-time code prompting is often unnatural for non-expert users of LLMs."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Demonstrations
Rater population
Mixed
Expertise required
Math, Coding
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Despite rapid advances in the capabilities of Large Language Models (LLMs), they continue to struggle with following relatively simple and unambiguous instructions, particularly when compositional structure is involved.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Despite rapid advances in the capabilities of Large Language Models (LLMs), they continue to struggle with following relatively simple and unambiguous instructions, particularly when compositional structure is involved.
  • Recent work suggests that models may follow instructions more effectively when they are expressed in pseudo-code rather than natural language.
  • However, writing pseudo-code programs can be tedious, and relying on few-shot demonstrations or inference-time code prompting is often unnatural for non-expert users of LLMs.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Expert verification evaluation

Contribution summary

  • To overcome these limitations, we propose a training time approach that fine-tunes LLMs using instruction-tuning data augmented with pseudo-code representations of natural language instructions paired with final responses.
  • We evaluate our method on 12 publicly available benchmarks spanning instruction-following, mathematical reasoning, and commonsense reasoning, across six base models.
  • Our results show that models trained with pseudo-code follow instructions more reliably, achieving relative gains of 8-21\% on instruction following benchmarks, while largely preserving and in some cases improving performance on…

Why it matters for eval

  • We evaluate our method on 12 publicly available benchmarks spanning instruction-following, mathematical reasoning, and commonsense reasoning, across six base models.
  • Our results show that models trained with pseudo-code follow instructions more reliably, achieving relative gains of 8-21\% on instruction following benchmarks, while largely preserving and in some cases improving performance on…

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Demonstrations

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.