HFEPX · Eval paper review

HAL: Inducing Human-likeness in LLMs with Alignment

Masum Hasan, Junjie Zhao, Ehsan Hoque

Published

Jan 6, 2026

Citations

0

Trust level

High

Usefulness score

67/100 (Medium)

Extraction confidence

75% (High)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Jul 1, 2026

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Human Eval Medicine Pairwise Preference

Should you rely on this paper?

This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.

Use this as a practical starting point for protocol research, then validate against the original paper.

Best use

Secondary protocol comparison source

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

No major weakness surfaced.

Human feedback signal

Detected

From extracted signals

Evaluation signal

Detected

Eval setup described

Usefulness for eval research

67/100

Moderate-confidence candidate

Useful as a secondary reference; validate protocol details against neighboring papers.

If you are doing eval pipeline work, start here

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

Abstract

Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize. As a result, improvements in human-like behavior are largely driven by scale or broad supervised training, rather than targeted alignment. We introduce Human Aligning LLMs (HAL), a framework for aligning language models to conversational human-likeness using an interpretable, data-driven reward. HAL derives explicit conversational traits from contrastive dialogue data, combines them into a compact scalar score, and uses this score as a transparent reward signal for alignment with standard preference optimization methods. Using this approach, we align models of varying sizes without affecting their overall performance. In large-scale Chatbot Arena-style human evaluations, a model aligned with HAL is more frequently perceived as human-like in conversation. Because HAL operates over explicit, interpretable traits, it enables inspection of alignment behavior and diagnosis of unintended effects. More broadly, HAL demonstrates how soft, qualitative properties of language--previously outside the scope for alignment--can be made measurable and aligned in an interpretable and explainable way.

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

strong

Pairwise Preference

Directly usable for protocol triage.

"Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize."

Evaluation Modes

strong

Human Eval

Includes extracted eval setup.

"Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize."

Benchmarks / Datasets

strong

LMSYS Chatbot Arena

Useful for quick benchmark comparison.

"Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize."

Benchmarks and datasets

LMSYS Chatbot Arena

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details

Uses human feedback: Yes
Feedback types: Pairwise Preference
Rater population: Not reported
Unit of annotation: Scalar
Expertise required: Medicine

Evaluation details

Evaluation modes: Human Eval
Agentic eval: None
Quality controls: Not reported
Evidence quality: High
Use this page as: Secondary protocol comparison source

Research brief

Metadata summary

Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize.

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

Key takeaways

Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize.
As a result, improvements in human-like behavior are largely driven by scale or broad supervised training, rather than targeted alignment.
We introduce Human Aligning LLMs (HAL), a framework for aligning language models to conversational human-likeness using an interpretable, data-driven reward.

Researcher actions

Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
Validate inferred eval signals (Human evaluation) against the full paper.
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

Pairwise preference evaluation

Contribution summary

Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize.
As a result, improvements in human-like behavior are largely driven by scale or broad supervised training, rather than targeted alignment.
We introduce Human Aligning LLMs (HAL), a framework for aligning language models to conversational human-likeness using an interpretable, data-driven reward.

Why it matters for eval

Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize.
We introduce Human Aligning LLMs (HAL), a framework for aligning language models to conversational human-likeness using an interpretable, data-driven reward.

Researcher checklist

Human feedback protocol is explicit

Detected: Pairwise Preference
Evaluation mode is explicit

Detected: Human Eval
Quality control reporting appears

No calibration/adjudication/IAA control explicitly detected.
Benchmark or dataset anchors are present

Detected: LMSYS Chatbot Arena
Metric reporting is present

No metric terms extracted.