Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note3 min read

VIBE: A VAD-Informed Benchmark for Entity-Centered Affective Profiling of Large Language Model Outputs

Source

arXiv

arxiv.org/abs/2608.03810v1

Read the source

Source

Plain-Language Brief

The text introduces VIBE, a new benchmark for analyzing the emotional tone of language models' outputs. The benchmark measures valence (positive or negative), arousal (exciting or calm), and dominance (powerful or vulnerable) in a space called Valence-Arousal-Dominance (VAD). It also introduces an 'Affective Passport' to report these profiles. The text provides evidence for three key findings: 1) valence, arousal, and dominance are distinct emotional dimensions, 2) the overall emotional tone of a text may differ from the tone used to describe specific entities within it, and 3) the context in which a text is generated can affect its emotional profile.

What the Document Says

  • The text introduces VIBE, a benchmark for entity-centered affective profiling of LLM outputs in Valence-Arousal-Dominance (VAD) space.
  • It presents a measurement contract for VIBE, which separates generation from external scoring and reports profiles through an Affective Passport.
  • The text provides empirical evidence for three key findings:
  • H1 shows that scalar favorability does not subsume arousal and dominance.
  • H2 shows that whole-response and target-directed VAD are different contracts.
  • H3 is a protocol-drift diagnostic, which motivates context metadata in every affective report.

Key Concepts Mentioned

  • VIBE
  • Valence-Arousal-Dominance (VAD)
  • affective profiling
  • language models
  • sentiment
  • favorability
  • emotion benchmarks
  • Affective Passport

Claims Made by the Document

  • VIBE is a new benchmark for entity-centered affective profiling of LLM outputs.
  • Valence, arousal, and dominance are distinct emotional dimensions.
  • The same text can carry one overall affective tone while representing the named target differently.
  • Context in which a text is generated affects its emotional profile.

Evidence Used

  • Human cross-validation of valence findings with rV = 0.944 and rV = 0.954
  • Arousal and dominance are directional estimates among human annotators, not point-precise
  • The same text carrying different affective tones for overall and specific entities.
  • Protocol drift diagnostic motivating context metadata in every affective report.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Large language models routinely describe socially salient targets, including political figures, countries, religions, organizations, historical events, and social groups, encoding affective framing alongside factual content: a target may appear favorable or threatening, calm or conflictual, powerful or vulnerable. Existing work captures parts of this space through sentiment, favorability, and emotion benchmarks, but none combines target-directed VAD attribution, an explicit scorer contract, and a passport reporting format. We introduce VIBE, a benchmark for entity-centered affective profiling of LLM outputs in Valence-Arousal-Dominance (VAD) space. Its core contribution is a measurement contract: VIBE separates generation from external scoring, distinguishes scalar favorability, response-level VAD, and target-directed VAD, and reports profiles through an Affective Passport. Three empirical layers support the contract. H1 shows scalar favorability does not subsume arousal and dominance: valence findings are cross-validated (rV = 0.944 judge-human, rV = 0.954 inter-scorer); arousal and dominance are single-scorer directional estimates, not point-precise, consistent with known inter-annotator difficulty on these axes (rA = 0.495, rD = 0.702 among human annotators). H2 shows whole-response and target-directed VAD are different contracts: the same text can carry one affective tone overall while representing the named target differently. H3 is a protocol-drift diagnostic: elicitation conditions shift profiles, motivating context metadata in every affective report. These results motivate entity-centered affective profiling as a documented practice: profiles should be released with scorer identity, coverage, protocol, and interpretation limits.