---
title: 'WildClaims: Implicit Facts in LLM Conversations'
url: https://www.emergentmind.com/topics/wildclaims
type: topic
---

# WildClaims: Implicit Facts in LLM Conversations

WildClaims is a dataset and problem formulation for studying **implicit information access** in large language model conversations. Derived from WildChat, it consists of **121,905 extracted factual claims** from **7,587 system utterances** in **3,000** conversations, each annotated for check-worthiness to estimate when conversational outputs should trigger retrieval, verification, or fact-checking workflows [2509.17442]. Its central contribution is conceptual as much as empirical: it treats factual transfer in dialogue not as a phenomenon confined to explicit question answering, but as something that also occurs in creative writing, editing, planning, role playing, and other interactions whose overt intent is non-informational. In that sense, WildClaims sits at the intersection of conversational IR, factual claim extraction, check-worthiness detection, and conversational factuality analysis.

## 1. Conceptual scope and definition

WildClaims was introduced to answer the question **“What do real-world information access conversations with LLMs actually look like?”** [2509.17442]. The work distinguishes two regimes. **Explicit information access** corresponds to the conventional IR setting in which a participant states a clear information need and the system responds with retrieved or generated content. **Implicit information access** arises when a system makes factual assertions during interactions whose primary purpose is not overtly informational, such as creative writing or drafting. The dataset operationalizes the latter regime.

Within this framing, a **factual claim** is a self-contained statement that asserts something about the world and is potentially verifiable against external sources. A **check-worthy factual claim** is a factual assertion that is worth checking and deemed necessary to be verified through external sources [2509.17442]. The point is not merely that the statement has truth conditions, but that uncritical reliance on it could be consequential. The paper’s examples include a fabricated legal citation, claims about tool capabilities, and historically questionable statements such as a 14th-century “Bushido code” [2509.17442].

The authors also propose a clarified definition of an **information access conversation (in the wild)**: a conversation in which information needs of participants are satisfied by exchanging **verified** information, using all necessary resources for **reliable knowledge transfer**, regardless of whether the user’s original goal was implicitly or explicitly information-seeking [2509.17442]. This directly contests a common narrowing of conversational IR to overt search-like dialogues. In WildClaims, the relevant trigger for information access is not the user’s surface intent alone, but the system’s production of check-worthy assertions.

## 2. Corpus construction and annotation pipeline

WildClaims is a derived resource built from **WildChat**, described as a corpus of **over 1M real user–ChatGPT logs** [2509.17442]. The construction pipeline first retains **English** conversations, yielding **478,498** English conversations, then filters out **math/coding-focused** interactions using **GPT-4.1-mini** with a prompt from Zhang et al. (2024); **79.6%** of the English subset is non-math/non-coding [2509.17442]. From this filtered pool, **3,000 conversations** are sampled.

The resulting sample contains **15,174 utterances** in total, including **7,587 system utterances**, and conversations are mostly short with an **average of 2.52 utterances per conversation** [2509.17442]. System outputs are comparatively long, with an **average of 219 words**, a property that matters because long-form responses often contain many atomic factual units rather than a single claim [2509.17442]. User intents were categorized via **GPT-4.1** following the WildBench taxonomy: **information seeking 33.5%**, **creative writing 18.8%**, **editing 15.8%**, and **reasoning 9.9%**, with role playing, planning, brainstorming, advice seeking, data analysis, and other categories comprising the remainder [2509.17442].

Claim extraction is performed only on system utterances. Two LLM-based extractors are used. **\(F_{Huo}\)**, adapted from Huo et al. (2023), extracts **self-contained factual claims**. **\(F_{Song}\)**, adapted from Song et al. (2024, VeriScore), targets **verifiable claims** [2509.17442]. Both use **GPT-4.1** and are modified to incorporate conversation history so that pronouns and ellipses can be resolved during decontextualization. Formally, given a system utterance \(u_s\) and history \(h\), extraction returns a set
\[
\mathbf{A} = \{a_1, a_2, \dots, a_n\} = F(u_s, h).
\]

Check-worthiness is then assigned using two classifiers: **\(CW_{Hassan}\)**, reflecting ClaimBuster-style prompting, and **\(CW_{Majer}\)**, based on optimized prompts designed to better capture annotation guidelines [2509.17442]. Both are implemented with **GPT-4.1** and use conversation context. The paper further defines:
\[
CW_{\text{Intersection}}(a,h) = CW_{Majer}(a,h) \land CW_{Hassan}(a,h),
\]
\[
CW_{\text{Union}}(a,h) = CW_{Majer}(a,h) \lor CW_{Hassan}(a,h).
\]

Manual validation covers **200 claims**: **100** sampled from \(F_{Huo}\) and **100** from \(F_{Song}\), each from a distinct conversation [2509.17442]. Two authors independently annotate the claims as CW/not CW, with a third author serving as tie-breaker. Reported **Cohen’s \(\kappa\)** is **0.672** for \(F_{Huo}\) and **0.580** for \(F_{Song}\), indicating that the notion of conversational check-worthiness is non-trivial but sufficiently stable for empirical study [2509.17442].

## 3. Formalization and empirical properties

WildClaims formalizes claim-level check-worthiness as a binary decision over an extracted factual claim \(a\) given discourse history \(h\):
\[
l = CW(a,h), \qquad l \in \{0,1\}.
\]
An utterance is **check-worthy** if it contains at least one extracted claim with \(CW(a,h)=1\), and a conversation is **check-worthy** if it contains at least one such utterance [2509.17442]. This moves the unit of analysis from document-level or utterance-level heuristics to explicit claims embedded inside responses.

The two extractors differ sharply in aggressiveness. **\(F_{Huo}\)** produces **31,108** claims, or **4.1 claims per utterance** and **10.4 claims per conversation** on average; **45.6%** of utterances and **45.1%** of conversations contain at least one extracted fact under this method [2509.17442]. **\(F_{Song}\)** produces **90,797** claims, or **12.0 claims per utterance** and **30.3 claims per conversation**; **72.1%** of utterances and **79.0%** of conversations contain at least one extracted fact [2509.17442]. The difference is not incidental: it demonstrates that conversational factuality estimates are heavily conditioned on the granularity of extraction.

Human validation confirms that a large fraction of extracted claims are actually check-worthy. The aggregated gold labels yield **40%** CW claims for \(F_{Huo}\) and **64%** for \(F_{Song}\) [2509.17442]. The paper derives a conservative lower-bound estimate of the proportion of conversations with at least one check-worthy claim by multiplying \(x\), the human-estimated proportion of extracted claims that are check-worthy, by \(y\), the proportion of conversations containing at least one extracted claim. This produces **18%** for \(F_{Huo}\) and **51%** for \(F_{Song}\) [2509.17442]. Because conversations often contain many claims rather than at most one, these are explicitly characterized as lower bounds.

Among the automatic check-worthiness methods, **\(CW_{\text{Union}}\)** obtains the best F1 and the highest \(\kappa\) across both extractors [2509.17442]. For \(F_{Huo}\), it reaches **P = 0.613**, **R = 0.950**, **F = 0.745**, and **\(\kappa = 0.504\)**. For \(F_{Song}\), it reaches **P = 0.769**, **R = 0.938**, **F = 0.845**, and **\(\kappa = 0.478\)** [2509.17442]. Using the strongest pipeline reported, **\(F_{Song} + CW_{\text{Union}}\)**, **91.8%** of extracted claims are marked CW, **70.2%** of system utterances contain at least one CW claim, **76.4%** of conversations contain at least one CW claim, and the average is **11 CW claims per conversation** [2509.17442].

A key empirical implication is that check-worthy assertions are not confined to explicitly informational tasks. With **\(F_{Song} + CW_{\text{Union}}\)**, **creative writing** still shows **57.7%** of system utterances as check-worthy; with **\(F_{Huo}\)** the corresponding figure is **41.3%** [2509.17442]. This is the central evidence for the paper’s broader thesis: real-world LLM conversations often function as information transfer even when neither side is conducting canonical search.

## 4. Relation to claim span identification and multilingual claim extraction

WildClaims extracts decontextualized claims from full system utterances, but adjacent work shows that claim detection can also be framed as **span identification**. The **Claim Span Identification (CSI)** task is defined as extracting the specific part or parts of a text that **“minimally represent a claim-like statement”**, operationalized as token-level binary labeling over a post \(x=(t_1,\dots,t_n)\) with labels \(y_i \in \{0,1\}\) indicating whether token \(t_i\) belongs to a claim span [2411.19579]. CSI differs from binary claim classification by providing exact span boundaries rather than a post-level claim/non-claim label, and is motivated as more challenging and more useful for downstream fact-checking, retrieval, argument mining, and explanation [2411.19579].

The multilingual literature is especially relevant. **X-CLAIM** introduces roughly **7K real-world claims** from social media in **English, Hindi, Punjabi, Tamil, Telugu, and Bengali**, framing CSI as **sequence labeling** with token-level **IO** tags; the paper reports that **IO** outperformed BIO, BEO, and BEIO encodings in its experiments [2310.18205]. It also reports that **multilingual training is best overall** relative to monolingual training, zero-shot transfer from English, and training on translated data, while prompted GPT-series models **underperform the smaller encoder-only language models for low-resource languages** [2310.18205]. For GPT-4, **10-shot** in-context prompting improves F1 by **~2.78 points on average** over zero-shot under the LANGUAGE prompt [2310.18205].

The **ICPR 2024 Competition on Multilingual Claim-Span Identification** extends the span-level paradigm with **HECSI**, containing **about 8K posts in English and about 8K posts in Hindi** with human-marked claim spans [2411.19579]. The competition paper explicitly states that, for a system like **WildClaims**, CSI is the component that highlights the “wild” bit in a post and enables targeted verification workflows [2411.19579].

This suggests that WildClaims and CSI address adjacent granularities of the same problem. WildClaims identifies self-contained, decontextualized factual assertions in long conversational outputs; CSI localizes minimal claim-bearing segments within a source text. A plausible implication is that a span-localization layer of the CSI type could refine WildClaims-style pipelines by marking exact token spans inside long system utterances before decontextualization, especially if the framework is extended beyond English.

## 5. Downstream claim organization: comparison, clustering, and repeated assertions

WildClaims focuses on extracting and labeling factual claims in conversation, but downstream fact-checking requires organization of those claims by relation and redundancy. **ClaimDiff** addresses one part of this problem by modeling how pairs of claims on the same contentious issue can **strengthen** or **weaken** one another, while also extracting the textual **rationale** responsible for that relation [2205.12221]. The relation is directional and explicitly distinguished from canonical fact verification: the task assumes that, among trusted sources, one argument is **not necessarily more true than the other**, so the goal is comparison rather than truth assessment [2205.12221]. This is important for conversational outputs because two system-generated claims may both be factually consistent while still framing an issue differently.

A second downstream task is claim clustering. **MultiClaimNet** introduces three multilingual cluster datasets spanning **86 languages**, with the largest component containing **85.3K fact-checked claims written in 78 languages** [2503.22280]. Claims are clustered when they discuss the **same underlying fact**, with clusters formed as **connected components** in a similarity graph built from pairwise claim matches [2503.22280]. The resource is motivated by the observation that fact-checking pipelines face massive redundancy: the same claim reappears across platforms, languages, and paraphrases. The paper reports that **50%** of claims in a cluster are repeated within **1.6 days** of the first occurrence and **75%** within **29.3 days** [2503.22280].

The connection to WildClaims is direct at the systems level. A plausible implication is that extracted conversational claims can be routed into ClaimDiff-style relational analysis when multiple claims on a contentious issue coexist, and into MultiClaimNet-style clustering when the goal is to detect repetition, reuse verdicts, or prioritize verification at the cluster level rather than the claim-instance level. This would align WildClaims with the broader fact-checking workflow in which claim detection precedes retrieval, triage, and verdict propagation.

## 6. Extensions, limitations, and adjacent infrastructures

WildClaims is deliberately scoped: it studies **English**, **non-coding/non-math** conversations sampled from WildChat, and its manual validation set contains **200 claims** [2509.17442]. The resource therefore characterizes a substantial but bounded portion of LLM-mediated discourse. Another limitation is methodological sensitivity: the difference between \(F_{Huo}\) and \(F_{Song}\) shows that the observed prevalence of conversational factuality depends strongly on the extractor’s recall-granularity trade-off [2509.17442]. The work’s own presentation acknowledges this by reporting both conservative lower bounds and less conservative estimates.

Adjacent research indicates how the WildClaims agenda may broaden. **HintsOfTruth** defines **multimodal checkworthiness detection** over image–claim pairs and introduces a dataset with **27K real-world and synthetic image/claim pairs** [2502.11753]. Its experiments show that well-configured lightweight text-based encoders can perform comparably to multimodal models, but primarily by focusing on non-claim-like content; multimodal LLMs can be more accurate yet incur substantial computational cost, while multimodal models are more robust on synthetic data [2502.11753]. This suggests that if WildClaims is extended to multimodal conversational settings, check-worthiness will likely need to be treated as a joint function of textual assertion and visual grounding rather than text alone.

A different adjacent direction concerns evidence-bearing claims processes rather than open-domain chat. **SoverClaim** combines **Hyperledger Indy** for self-sovereign identity with **Storj** for decentralized document storage in document-based claims workflows such as proof of residence or medical-image verification, reporting **response times of under 750 ms** [2411.16987]. WildClaims is not a decentralized identity system, but the juxtaposition is instructive: once conversational claims move from detection to evidence exchange, provenance, selective disclosure, storage, and deletion become operational concerns rather than purely linguistic ones.

In the current literature, WildClaims therefore occupies a specific but consequential niche. It is not a truth-label corpus, not a span-level sequence-tagging benchmark, and not a multilingual claim cluster dataset. Instead, it is a resource for quantifying how often LLM conversations themselves become sites of factual transfer that should trigger verification. Its principal significance lies in reframing conversational factuality as an **information access** problem even when the conversation does not outwardly resemble search [2509.17442].

Source: https://www.emergentmind.com/topics/wildclaims