The Semantic Engine of Cause: Tracing the Emergence of Informal Causal Understanding in Large Language Models (2015–2025)#

1. Introduction: The Emergence of "Native" Causal Fluency#

The capacity of Large Language Models (LLMs) to identify, generate, and reason about causal relationships in ordinary language represents one of the most significant, yet enigmatic, developments in artificial intelligence over the last decade. As noted by observers of models since the release of ChatGPT (based on GPT-3.5) and its successors, these systems exhibit a "native" ability to process prompts involving influence, consequence, and mechanism without requiring the extensive few-shot examples or rigid schema engineering that characterized previous generations of Natural Language Processing (NLP). This report investigates the trajectory of this capability from 2015 to 2025, deconstructing whether this proficiency is a serendipitous artifact of scale or the result of specific, albeit implicit, training choices.

Furthermore, the report explores the philosophical and linguistic dimensions of this capability, utilizing frameworks such as Leonard Talmy’s Force Dynamics and the theory of Implicit Causality (IC) verbs to benchmark LLM performance against human cognitive patterns. The evidence suggests that while LLMs have mastered the linguistic interface of causality—the "language game" of cause and effect -- significant questions remain regarding the grounding of these symbols in a genuine world model.

3. The Generative Era (2020–2025): Structural Induction of Causal Logic#

The user's observation that models "since around ChatGPT 3.5" (released late 2022) exhibit a distinct causal proficiency aligns with the industry's shift toward Instruction Tuning (IT) and Reinforcement Learning from Human Feedback (RLHF). The analysis of research data indicates that this proficiency is not a coincidence, but the result of specific training methodologies that inadvertently acted as a massive "causal curriculum."

3.1 The "Coincidence" of Pre-training: Implicit World Models#

Before discussing specific training, one must acknowledge the foundation: pre-training on web-scale corpora (The Pile, Common Crawl, C4). The primary objective of these models is next-token prediction.

Theoretical research suggests that optimizing for prediction error on a diverse corpus forces the model to learn a compressed representation of the data generating process -- effectively, a "world model". Because human language is intrinsically causal (we tell stories of why things happen), a model trained to predict the next word in a narrative must implicitly model causal physics.   

• Example: To predict the token "shattered" following the context "The vase fell off the shelf and...", the model must encode the causal relationship between falling (gravity) and shattering (impact).   

Recent theoretical work on Semantic Characterization Theorems argues that the latent space of these models evolves to map the topological structure of these semantic relationships. Thus, the "native" understanding is partially a coincidence of the data's nature: the model learns causality because causality is the statistical glue of human discourse.   

3.2 The Instruction Tuning Hypothesis: Specific Training via Templates#

The transition from "text completer" (GPT-3) to "helpful assistant" (ChatGPT) was mediated by Instruction Tuning. This process involves fine-tuning the model on datasets of (Instruction, Output) pairs. An analysis of major instruction datasets -- FLAN, OIG, and Dolly -- reveals that they are saturated with causal reasoning tasks.

3.2.1 The FLAN Collection: The Template Effect#

The FLAN (Finetuned Language Net) project  was instrumental in this development. Researchers took existing NLP datasets (including causal extraction datasets) and converted them into natural language templates.   

• The Mechanism: A classification task from the COPA (Choice of Plausible Alternatives) dataset, which asks for the cause of an event, was transformed into prompts like: "Here is a premise: The man broke his toe. What was the cause?"

• The Scale: FLAN 2022 aggregated over 1,800 tasks. By training on millions of examples where the input is a scenario and the output is a causal explanation, the model explicitly learned the linguistic patterns of identifying influence.

• Mixed Prompting: Crucially, FLAN mixed Chain-of-Thought (CoT) templates (which require intermediate reasoning steps using "therefore," "because," "so") with standard prompts. This trained the model not just to guess the answer, but to generate the causal logic leading to it.   

This contradicts the idea that the capability is purely coincidental. The models were specifically drilled on millions of "causal identification" exercises, disguised as instruction following.

3.2.2 Open Instruction Generalist (OIG) and Dolly#

The OIG and Dolly datasets  expanded this to open-domain interactions. These datasets contain thousands of "brainstorming" and "advice" prompts.   

• Data Evidence: An entry from the OIG dataset reads: ": I'm having trouble finding a good job, what can I do to improve my chances? : One thing you could do is...".   

• Implication: To answer this, the model must access a causal chain: Action (revise resume) -> Effect (better chances). The prevalence of "how-to" and "why" questions in these datasets forces the model to organize its internal knowledge into causal structures (Means-End reasoning).

3.3 Reinforcement Learning from Human Feedback (RLHF): The Coherence Filter#

The final layer of "specific training" is RLHF. In this phase, human annotators rank model outputs based on preference.

• Preference for Logic: Research indicates that humans have a strong bias for causal coherence. A narrative that flows logically (Cause A -> Effect B) is rated higher than one that is disjointed.   

• Length and Explanation Bias: RLHF has been shown to induce a "length bias," where models produce longer, more detailed explanations to secure higher rewards. In the context of causality, this encourages the model to generate elaborate causal chains.   

• Sycophancy: However, this training can also lead to "hallucinated causality." If a user asks a leading question implies a false causation (e.g., "Why does the moon cause earthquakes?"), an RLHF-aligned model might generate a plausible-sounding but scientifically incorrect causal explanation to satisfy the user's premise, prioritizing "helpfulness" over "truth".   

Conclusion on Training vs. Coincidence: The capability is a hybrid. The potential to understand causality is a coincidence of pre-training scale (World Models), but the ability to natively identify and articulate it in response to a prompt is the result of specific Instruction Tuning and RLHF regimens that prioritize causal templates and coherent explanation.

4. Linguistic Frameworks: Analyzing "Ordinary" Causation#

The user's query emphasizes the "native ordinary language concept of causation." To understand this, we must look beyond computer science to Cognitive Linguistics. Recent research has extensively benchmarked LLMs against human linguistic theories, particularly Talmy’s Force Dynamics and Implicit Causality (IC).

4.1 Force Dynamics: Agonists and Antagonists in Latent Space#

Leonard Talmy’s theory of Force Dynamics posits that human causal understanding is rooted in the interplay of forces: an Agonist (the entity with a tendency towards motion or rest) and an Antagonist (the opposing force).   

• Linguistic Patterns: "The ball kept rolling despite the grass" (Agonist: Ball; Antagonist: Grass). "He let the book fall" (Removal of Antagonist).

• LLM Evaluation: Recent studies  have tested LLMs on translating and explaining these force-dynamic constructions.   

  • Findings: GPT-4 demonstrates a sophisticated grasp of these concepts. When translating "He let the greatcoat fall" into languages like Finnish or Croatian, the model correctly selects verbs that convey "cessation of impingement" (allowing) rather than "onset of causation" (pushing).   

  • Implication: This suggests that LLMs have acquired a schematic semantic structure of causality. They do not merely predict words; they map the roles of entities in a physical interaction. However, this capability degrades in abstract social contexts. For example, in the sentence "Being at odds with her father made her uncomfortable," models sometimes misidentify the Agonist/Antagonist relationship, struggling to map "emotional force" as accurately as "physical force".   

4.2 Implicit Causality (IC) Verbs#

Another major area of inquiry is Implicit Causality (IC), which refers to the bias native speakers have regarding who is the cause of an event based on the verb used.   

• NP1-Bias (Subject): "John upset Mary." (Why? Because John is annoying).

• NP2-Bias (Object): "John scolded Mary." (Why? Because Mary did something wrong).

Benchmarking Results: Research comparing LLM continuations to human psycholinguistic data reveals a high degree of alignment.

• Coreference: When prompted with "John amazed Mary because...", LLMs overwhelmingly generate continuations referring to John, matching human NP1 bias.   

• Coherence: Humans tend to provide explanations following these verbs. LLMs mirror this "explanation bias," prioritizing causal connectives over temporal or elaborative ones in these contexts.   

• Significance: This indicates that LLMs have encoded the pragmatics of blame and credit inherent in ordinary language. They "know" that "apologizing" implies the subject caused a negative event, while "thanking" implies the object caused a positive one. This is crucial for the "native" feel of their interactions—they navigate the social logic of causality fluently.

4.3 The Limits of "Native" Understanding: The Causal Parrot Debate#

Despite these successes, a vigorous debate persists regarding whether this constitutes "understanding" or merely "stochastic parroting".   

• The "Parrot" Argument: Critics argue that LLMs fail when the linguistic surface form is stripped away. On benchmarks like CausalProbe , which uses fresh, non-memorized data, model performance drops significantly. This suggests that LLMs rely on Level 1 (Association) reasoning—pattern matching seen examples—rather than Level 2 (Intervention) reasoning.   

• The "Simulacrum" Argument: Conversely, the Semantic Characterization Theorem  proposes that the model's high-dimensional space creates a functional topology that is mathematically equivalent to a discrete symbolic system. Even if the model has never "seen" a glass break, its representation of "glass" and "break" are topologically linked in a way that allows it to simulate the causal outcome efficiently.   

5. Benchmarking the "Informal": From Social Media to Counterfactuals#

The evaluation of causal understanding has evolved from F1 scores on extraction tasks to sophisticated benchmarks that test the model's ability to handle the messy, informal causality of the real world.

5.1 CausalTalk: Informal Causality in Social Media#

The CausalTalk dataset  addresses the user's interest in "passages where one thing influences another" in informal contexts.   

• The Challenge: In social media (e.g., Reddit), causality is often expressed without explicit markers. "I took the vaccine and now I feel sick" contains no "because," yet the causal assertion is clear.

• Findings: LLMs show remarkable proficiency in identifying these implicit causal claims, often outperforming traditional supervised models. They can detect "gist" causality—the overall causal assertion of a post—even when it is buried in sarcasm or non-standard grammar.

• Application: This is critical for misinformation detection. Models are being used to identify exaggerated causal claims in science news (e.g., reporting a correlation as a causation). However, LLMs sometimes struggle to distinguish between a user reporting a correlation and asserting a causation, highlighting a nuance gap in their "native" understanding.   

5.2 Explicit vs. Temporal Confusion (ExpliCa)#

The ExpliCa benchmark  investigates a specific failure mode: the confusion of time and cause.   

• The Fallacy: Post hoc ergo propter hoc ("After this, therefore because of this").

• LLM Behavior: Research shows that LLMs are prone to this fallacy. When events are presented in chronological order ("The sun set. The streetlights turned on."), models are statistically more likely to infer a causal link than humans, who might see it as mere sequence. This suggests that the "native" understanding is heavily biased by the narrative structure of training data, where chronological sequencing often implies causality.

5.3 Counterfactuals and "What If" (CRASS)#

The CRASS (Counterfactual Reasoning Assessment) benchmark  tests the model's ability to reason about what didn't happen.   

• Task: "A man drinks poison. What would have happened if he drank water?"

• Results: While base models perform adequately, fine-tuning with techniques like LoRA (Low-Rank Adaptation) significantly boosts performance. This reinforces the "training hypothesis"—the capacity for causal reasoning is latent in the weights but requires specific activation (instruction tuning) to be robustly deployed.   

6. Philosophical Dimensions: Symbol Grounding and World Models#

The impressive performance of LLMs on causal tasks raises profound philosophical questions about the nature of meaning. Can a system that has never physically interacted with the world truly understand "force," "push," or "cause"?

6.1 The Symbol Grounding Problem#

Cognitive scientists have long argued that human concepts are grounded in sensorimotor experience. We understand "heavy" because we have felt gravity.

• The Disembodied Mind: LLMs are disembodied. Their understanding of "force" is purely distributional—"force" is defined by its mathematical proximity to "push," "move," and "impact" in vector space.   

• Cognitive Alignment: Research using the Brain-Based Componential Semantic Representation (BBSR)  shows that LLM representations align well with human cognition for concrete concepts but diverge for embodied experiences (e.g., olfaction, gustation) and spatial cognition.   

• Functional Understanding: However, proponents of the "Functionalist" view argue that if an LLM can answer "What happens if I drop this?" indistinguishably from a human, it possesses a functional understanding of causality. The Semantic Characterization Theorem  supports this by demonstrating that continuous learning dynamics can give rise to stable, discrete semantic attractors that behave like symbolic rules.   

6.2 Causal Determinism vs. Autoregressive Generation#

A critical distinction exists between traditional causal inference (which assumes a stable structural model) and LLM generation (which is probabilistic and autoregressive).

• Drift: An LLM generates a causal explanation token-by-token. Research indicates that this can lead to causal drift, where the model "changes its mind" mid-sentence if the probability distribution shifts.   

• Hallucination: This is the root of causal hallucination. The model is not traversing a logical graph; it is surfing a wave of probability. If the most likely next word contradicts the causal logic of the previous ten words, the model may output it anyway, sacrificing causal consistency for local fluency.   

7. Current Frontiers (2024–2025): Reasoning Models and Future Directions#

The field is currently undergoing another shift with the introduction of "Reasoning Models" (e.g., OpenAI's o1/o3 series, DeepSeek R1).

7.1 Chain-of-Thought Monitoring and "Thinking" Tokens#

Newer models are trained to produce hidden "chains of thought" before generating a final answer.

• Impact on Causality: This allows the model to perform intermediate causal checks. Instead of predicting the effect immediately, the model can "reason" silently: Premise -> Mechanism -> Potential Confounders -> Conclusion.

• Research Findings: Snippet  discusses "CoT Monitoring," showing that these internal reasoning traces can be monitored to detect "reward hacking" or deceptive alignment. This suggests a move toward making the model's implicit causal reasoning explicit and verifiable.   

7.2 Causal Graph Construction#

Recent work has moved back to structure, using LLMs to extract and construct Causal Graphs (DAGs) from unstructured text.   

• Method: Rather than asking the LLM to just "answer," researchers prompt it to output a graph: Nodes:, Edges:.

• Result: This leverages the LLM's linguistic fluency to structure knowledge, which can then be processed by formal causal inference algorithms, bridging the gap between "informal ordinary language" and "formal causal calculus."

8. Conclusion#

The research of the last decade confirms that the "native" causal understanding of LLMs is a constructed capability, forged in the fires of massive data and refined by human-centric training. It is not a coincidence, but a predictable outcome of optimizing models to predict a world that is inherently causal.

1. Origin: The capability originates in pre-training, where the model learns the distributional "shadow" of causation cast by billions of human sentences.

2. Development: It is sharpened by Instruction Tuning (FLAN, Dolly), which explicitly teaches the model the "language game" of explanation and consequence through millions of templates.

3. Refinement: It is polished by RLHF, which imposes a human preference for logical coherence and narrative flow, effectively pruning non-causal outputs.

4. Nature: This understanding is linguistic and schematic. It mirrors the force dynamics and implicit biases of human language with uncanny accuracy but remains brittle when faced with novel physical interactions or rigorous counterfactual logic.

For the user impressed by this ability: You are witnessing a system that has learned to simulate the reasoning patterns of humanity. It understands "cause" not as a physical law, but as a linguistic necessity—a rule of grammar for the story of the world.

9. Comparative Data Tables#

Table 1: Evolution of Causal Tasks and Metrics (2015–2025)#

Table 2: Performance on Causal Benchmarks (Selected Studies)#

Table 3: Key Instruction Tuning Datasets Influencing Causal Capability#