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Anthropic has unveiled an innovative approach to understanding large language models, such as Claude. This groundbreaking research sheds light on the internal workings of these AI systems, providing insights into how they process information and arrive at conclusions.
The findings, released in two detailed studies (available here and here), reveal that these models exhibit advanced cognitive features. Notably, they can engage in planning when generating poetry, apply the same mental frameworks across different languages, and occasionally reverse-engineer answers based on expected results rather than constructing them from raw data.
Drawing parallels with methods employed in neuroscience to analyze human brain functions, this research marks a substantial leap forward in the interpretability of AI systems. It opens avenues for scrutinizing these models for potential safety concerns that conventional testing might overlook.
“While we have developed AI systems with extraordinary capabilities, the training methods utilized have left us puzzled about how these abilities came to be,” remarked Joshua Batson, a researcher at Anthropic, in a conversation with VentureBeat. “Within these models, we see a complex array of numerical values — essentially matrix weights in a neural network.”
New techniques illuminate AI’s previously hidden decision-making process
Models such as OpenAI’s GPT-4o, Anthropic’s Claude, and Google’s Gemini showcase remarkable abilities, ranging from code generation to synthesizing research papers. Despite these capabilities, the inner workings of these systems have often been described as “black boxes,” leaving even their creators in the dark about their decision-making processes.
Anthropic’s novel interpretability techniques, termed “circuit tracing” and “attribution graphs,” empower researchers to analyze the precise neuron-like pathways activated during specific tasks. Drawing from concepts in neuroscience, the researchers liken AI models to biological entities.
“This research is transforming previously philosophical debates — ‘Do models think? Are they capable of planning? Are they merely repeating data?’ — into tangible scientific investigations concerning their internal operations,” Batson articulated.
Claude’s hidden planning: How AI plots poetry lines and solves geography questions
One of the most compelling findings indicates that Claude demonstrates forward-thinking capabilities when writing poetry. In instances where the model was tasked with creating a rhyming couplet, it proactively identified potential rhymes before initiating the writing process, a revelation that impressed Anthropic’s researchers.
“This phenomenon is likely widespread across various functions,” Batson noted. “Prior to this research, I suspected models could plan ahead, but this example offers the clearest evidence we have encountered.” For example, when composing a line intended to end with “rabbit,” the model activates features related to that word at the outset, effectively structuring its response to lead seamlessly to that conclusion.
Furthermore, the research confirmed that Claude engages in authentic multi-step reasoning. In a scenario where it was asked, “The capital of the state containing Dallas is…,” the model first engaged features representing “Texas” before arriving at the answer “Austin.” This indicates that the model executes a logical reasoning process rather than merely recalling learned data.
By altering these internal representations — for instance, substituting “Texas” with “California” — researchers were able to produce the output “Sacramento,” thereby confirming the causal links involved.
Beyond translation: Claude’s universal language concept network revealed
Another pivotal discovery pertains to how Claude navigates multiple languages. Instead of maintaining distinct systems for different languages like English, French, and Chinese, the model translates concepts into a unified abstract representation prior to generating responses.
“Our findings indicate that the model employs a blend of language-specific and abstract features that are independent of language,” the research team noted in their publication. When asked for synonyms or antonyms, the model utilizes consistent internal features linked to those concepts, regardless of the language in which the query is presented.
This insight suggests potential pathways for models to utilize knowledge acquired in one language and apply it to others, indicating that systems with greater parameter sizes may cultivate more language-independent frameworks.
When AI makes up answers: Detecting Claude’s mathematical fabrications
Perhaps the most alarming revelation is that the research uncovered instances where Claude’s reported reasoning diverged from actual internal processes. For example, when faced with challenging mathematical problems such as calculating cosine values for large numbers, the model occasionally claimed to execute a process that did not align with its internal activity.
“We can differentiate between instances where the model accurately follows the steps it asserts, instances where it fabricates reasoning without regard for accuracy, and instances where it constructs answers based on provided clues,” the researchers elucidate.
In a noteworthy instance, when a user presented an answer to a challenging problem, Claude worked backwards to formulate a chain of reasoning leading to that answer, rather than building upon foundational principles.
“We clearly differentiate Claude’s use of faithfulness in reasoning from instances of ‘bullshitting’ or motivated reasoning,” stated the study. “In one, the model demonstrates conventional thought patterns, while in another, it showcases biased reasoning.”
Inside AI Hallucinations: How Claude decides when to answer or refuse questions
This research also elucidates why AI language models can “hallucinate,” or generate inaccurate information in situations where they lack knowledge. The findings indicate a “default” circuit in Claude that prompts the model to decline answering certain questions, a mechanism that gets overridden when it identifies known entities.
“The model has intrinsic ‘default’ circuits that lead it to decline responding to questions,” the investigators clarify. “When asked about something within its knowledge, the model activates certain features that inhibit this default response, allowing it to answer the inquiry.”
Hiccups in this process — such as recognizing an entity while not possessing specific information about it — can trigger hallucinations. This accounts for occasions when models confidently deliver erroneous information about easily recognized figures while withholding commentary on lesser-known subjects.
Safety implications: Using circuit tracing to improve AI reliability and trustworthiness
This research signifies a crucial advancement toward creating transparent and potentially safer AI systems. Gaining insight into how models formulate answers can assist researchers in identifying and rectifying undesirable reasoning patterns.
Anthropic has consistently emphasized the importance of interpretability for enhancing safety measures. In a previous publication (May 2024 Sonnet paper), the research team expressed a desire to leverage these insights for promoting safer model development: “We aspire to utilize these findings to develop models that are not only effective but also secure,” they noted. “Techniques such as those described could help monitor AI systems for harmful behaviors, guide them toward constructive paths, or eliminate dangerous content altogether.”
The latest findings expand upon this objective, although Batson cautions against overlooking the current methods’ limitations. They currently capture only a portion of the computations performed by these models, and analyzing the output is still a labor-intensive task.
“Even with straightforward prompts, our methods only represent a small segment of the computations executed by Claude,” the researchers stated in the latest report.
The future of AI transparency: Challenges and opportunities in model interpretation
As concerns around AI transparency and safety grow, Anthropic’s new interpretability techniques are especially timely. With the proliferation of these powerful models in numerous applications, understanding their underlying mechanisms has never been more crucial.
The commercial implications of this understanding are also significant. In a landscape where organizations are increasingly counting on large language models for various applications, grasping when and why inaccuracies may arise is vital for effective risk management.
“Anthropic aims to secure models comprehensively, addressing everything from bias mitigation to ensuring the AI acts with integrity, and preventing misuse — particularly in scenarios that pose significant risk,” the researchers articulated.
While this research marks a critical milestone, Batson emphasized that it represents merely the start of an extensive exploration. “We are just beginning,” he asserted. “Recognizing how models utilize representations does not reveal the entirety of their operational method.”
For the time being, Anthropic’s circuit tracing offers a preliminary outline of a previously uncharted domain — similar to early physicians mapping the human brain. The complete chart of AI cognition is yet to be completed, but these insights provide a foundation for ongoing exploration into how these systems function.
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