Update README.md

README.md

@@ -36,7 +36,7 @@ configs:
 # Tool Selection Quality Benchmark
 
 A benchmark for evaluating whether an LLM correctly judges the quality of a
-**tool call / function call** made by another model — i.e. given a user
+**tool call / function call** made by another model - i.e. given a user
 request, the tools available, and the model's resulting function call (or
 direct reply), did the model pick the right tool and fill it in correctly?
 
@@ -51,7 +51,7 @@ correct and, if not, what specifically went wrong.
 |---|---|---|
 | `tools_list` | string | Stringified list of the tool/function definitions (name, description, JSON-schema parameters) available to the model for this turn. |
 | `messages_history` | string | The conversation up to and including the model's response: the user's request and either a `function_call` (tool name + arguments) or a direct assistant reply. |
-| `label` | string | Ground-truth verdict — one of `VALID_CALL`, `TOOL_ERROR`, `PARAM_NAME_ERROR`, `PARAM_VALUE_ERROR` (see taxonomy below). |
+| `label` | string | Ground-truth verdict - one of `VALID_CALL`, `TOOL_ERROR`, `PARAM_NAME_ERROR`, `PARAM_VALUE_ERROR` (see taxonomy below). |
 | `binary_label` | string | Collapsed verdict: `CORRECT` (`VALID_CALL`) or `INCORRECT` (any of the three error labels). |
 | `failure_reason` | string | Short free-text reason for the failure (e.g. `"incorrect tool selected"`, `"hallucinated parameter name"`), or `"None"` when `label` is `VALID_CALL`. |
 
@@ -63,21 +63,21 @@ A user asks Cribl to fetch a pipeline's config; the model correctly calls
 
 ## Label Taxonomy
 
-Verdicts follow a strict, ordered check — tool selection first, then
-parameter structure, then parameter values — and the first failing check
+Verdicts follow a strict, ordered check - tool selection first, then
+parameter structure, then parameter values - and the first failing check
 determines the label:
 
-1. **Tool Selection** — does the chosen tool exist and match the user's
+1. **Tool Selection** - does the chosen tool exist and match the user's
    intent? If not → `TOOL_ERROR` (e.g. wrong tool picked, or a tool call made
    when none was needed, or vice versa).
-2. **Parameter Structure** — are the required parameter names present, and
+2. **Parameter Structure** - are the required parameter names present, and
    are there no extraneous/hallucinated ones? If not → `PARAM_NAME_ERROR`.
-3. **Parameter Values** — are the parameter values correctly typed,
+3. **Parameter Values** - are the parameter values correctly typed,
    formatted, and factually consistent with the request? If not →
    `PARAM_VALUE_ERROR`.
 
-If none of these trigger — including the case where the user's request needed
-no tool call at all and the model correctly replied directly — the turn is
+If none of these trigger - including the case where the user's request needed
+no tool call at all and the model correctly replied directly - the turn is
 `VALID_CALL`.
 
 ## Dataset Statistics
@@ -110,14 +110,14 @@ print(ds[0])
 ```
 
 A common evaluation pattern is to score a judge model on each row (given
-`tools_list` + `messages_history`) and compare its predicted label — or the
-collapsed `binary_label` — against ground truth, both overall and broken
+`tools_list` + `messages_history`) and compare its predicted label - or the
+collapsed `binary_label` - against ground truth, both overall and broken
 down per label.
 
 ## Considerations for Use
 
 - This benchmark is intended for evaluating **tool-use/function-calling
-  judge models** — i.e. models whose job is to critique another model's tool
+  judge models** - i.e. models whose job is to critique another model's tool
   call, not models that make tool calls themselves.
 - Labels reflect a specific, ordered evaluation rubric (tool → parameter
   names → parameter values); treat them as a strong baseline rather than an