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HFEPX · Eval paper review

Motivation in Large Language Models

Omer Nahum, Asael Sklar, Ariel Goldstein, Roi Reichart

Published

Mar 15, 2026

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Mar 15, 2026

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General Pairwise Preference

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

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Abstract

Motivation is a central driver of human behavior, shaping decisions, goals, and task performance. As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation. We examine whether LLMs "report" varying levels of motivation, how these reports relate to their behavior, and whether external factors can influence them. Our experiments reveal consistent and structured patterns that echo human psychology: self-reported motivation aligns with different behavioral signatures, varies across task types, and can be modulated by external manipulations. These findings demonstrate that motivation is a coherent organizing construct for LLM behavior, systematically linking reports, choices, effort, and performance, and revealing motivational dynamics that resemble those documented in human psychology. This perspective deepens our understanding of model behavior and its connection to human-inspired concepts.

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

Pairwise Preference

Directly usable for protocol triage.

"Motivation is a central driver of human behavior, shaping decisions, goals, and task performance."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Motivation is a central driver of human behavior, shaping decisions, goals, and task performance."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Motivation is a central driver of human behavior, shaping decisions, goals, and task performance."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Motivation is a central driver of human behavior, shaping decisions, goals, and task performance."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Motivation is a central driver of human behavior, shaping decisions, goals, and task performance."

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
Pairwise Preference
Rater population
Not reported
Expertise required
General
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

Motivation is a central driver of human behavior, shaping decisions, goals, and task performance.

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

Key takeaways

  • Motivation is a central driver of human behavior, shaping decisions, goals, and task performance.
  • As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation.
  • We examine whether LLMs "report" varying levels of motivation, how these reports relate to their behavior, and whether external factors can influence them.

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

Contribution summary

  • Motivation is a central driver of human behavior, shaping decisions, goals, and task performance.
  • As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation.
  • Our experiments reveal consistent and structured patterns that echo human psychology: self-reported motivation aligns with different behavioral signatures, varies across task types, and can be modulated by external manipulations.

Why it matters for eval

  • Motivation is a central driver of human behavior, shaping decisions, goals, and task performance.
  • As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Pairwise Preference

  • 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.